Jun 14, 2017 - road safety and road capacity, as well as driver workload reduction. ..... Vision of development of automated truck platooning in Europe.
Automated Truck Platooning Opportunities and Barriers for Road Freight Transport in Europe Master’s thesis
Author: Martin Dimitrov Roshkev Supervisor: Ass. Prof. Mag. Dr. Petra Staufer-Steinnocher Date: 25.07.2017
Automated Truck Platooning Opportunities and Barriers for Road Freight Transport in Europe Master’s thesis
Author: Martin Dimitrov Roshkev Supervisor: Ass. Prof. Mag. Dr. Petra Staufer-Steinnocher Date: 05-Nov-17
Abstract Driving automation has been subject to continuous improvement in the recent years. In this context, automated truck platooning refers to columns of trucks that are virtually connected to travel together at close distance, steered by the driver of the first truck while the rest of the trucks rely on driving automation systems. Practical tests have revealed high potential for achieving benefits like fuel consumption and driver cost reduction, improved road safety and road capacity, as well as driver workload reduction. Moreover, truck platooning promises to mitigate the risk of the upcoming driver shortage problem and to decrease the overall negative impact caused by road freight transport. Although the European logistics industry has already recognized the opportunities of automated truck platooning, there are several barriers towards successful operation on public roads. The implementation can be hindered by technical and infrastructural limitations, but also by uncertainties concerning cost of investment, distribution of earnings, issues with cross-border and multibrand operation, technology adoption by human drivers, social reaction, as well as various legal and regulatory constraints. This research comprises a literature review and consultation with an industry partner from the road haulage sector to summarize the opportunities and barriers from four different points of view: economic, technical, human and legal. The thesis identifies that cooperation on industrial and political level needs to be encouraged to facilitate extensive testing on public roads. In addition, the research points out specific topics for further investigation that can open the doors for large-scale implementation of platooning in Europe.
Table of contents
Abstract .................................................................................................................................. 2 List of figures ........................................................................................................................ 4 List of tables .......................................................................................................................... 6 List of abbreviations .............................................................................................................. 7 Acknowledgements ............................................................................................................... 8 1.
Introduction ................................................................................................................. 9
Market and technology overview ............................................................................. 15 2.1 Road freight transport ........................................................................................... 15 2.2 Driving automation systems ................................................................................. 18 2.3 Automated truck platooning ................................................................................. 34 2.4 Stakeholders and timeline ..................................................................................... 40 2.5 Projects and road tests .......................................................................................... 45
Opportunities and barriers ....................................................................................... 55 3.1 Economic considerations ...................................................................................... 56 3.2 Technical considerations ...................................................................................... 65 3.3 Human considerations .......................................................................................... 74 3.4 Legal considerations ............................................................................................. 86
Consultation with industry partner ......................................................................... 96 4.1 Approach ............................................................................................................ 96 4.2 Interview ............................................................................................................ 97 4.3 Discussion ............................................................................................................ 99
Conclusions .............................................................................................................. 107
References ......................................................................................................................... 109
List of figures
Figure 1. Figure 2. Figure 3. Figure 4.
Figure 5. Figure 6.
Figure 7. Figure 8. Figure 9.
Figure 10. Figure 11. Figure 12. Figure 13. Figure 14. Figure 15. Figure 16. Figure 17. Figure 18. Figure 19. Figure 20.
Another view of SAE’s levels of automation – responsibilities of human and system (Eckhardt, 2016, p. 67) .................................................... 24 A. Vehicle and pedestrian detection; B. Traffic light detection (Mobileye, 2017) ............................................................................................ 29 A. Real-time updating HD road map (HERE, 2017); B. HD road map and driving patterns (Urmson, 2015) ............................................................. 29 A. Automated guided vehicles for horizontal transportation (Konecranes, 2017); B. Automated heavy truck platoon arriving at the Port of Rotterdam during the 2016 EU Truck Platooning Challenge (EU Truck Platooning Challenge, 2016) ........................................................ 30 The expected transition from ADAS to entirely automated driving (Renesas, 2017) .............................................................................................. 31 Example of a prototype road car with sensor technology for autonomous driving and the corresponding customer functions (Audi, 2014) ............................................................................................................... 32 Truck platoon with radar and lidar technology and wireless communication (Janssen et al., 2015, p. 7) .................................................... 34 Wind speed around platooning trucks calculated with computational fluid dynamics (Alam, 2014, p.46)................................................................. 35 Example of automation and information systems connected via CANbus on experimental vehicle part of the KONVOI project (Tsugawa et al., 2016, p. 72) ............................................................................................... 37 Technology for automated platooning on heavy cargo trucks (Green Car Congress, 2016) ....................................................................................... 37 Vision of development of automated truck platooning in Europe (Eckhardt, 2016, p. 173) ................................................................................. 42 EU Roadmap for Truck Platooning (ACEA, 2017d) ..................................... 43 Tests with platooning trucks in the desert (Lu & Shladover, 2011, p. 14) ................................................................................................................... 47 Modifications on a truck used in the PATH research project (Tsugawa et al., 2016, p. 74) ........................................................................................... 48 Auburn University – tests with platooning trucks (Roeth, 2013, p. 11) ......... 49 Tests with trucks as part of Energy ITS in Japan (SAE, 2017) ...................... 49 KONVOI project – platooning trucks on a highway in Germany (IKA, 2017) ............................................................................................................... 50 The automated truck platooning system used in the KONVOI project (Tsugawa et al., 2016, p. 72) .......................................................................... 50 SARTRE platooning trucks and passenger cars (Volvo, 2012) ..................... 51 Scania trucks during testing on public roads (COMPANION, 2014) ............ 52
Figure 21. Figure 22.
Figure 23. Figure 24.
Testing trucks are parked during the 2016 European Truck Platooning Challenge (EU Truck Platooning Challenge, 2016) ....................................... 53 Measurements of fuel savings of a two-truck platoon gathered during field tests as part of the California PATH project. (Tsugawa et al., 2016, p. 75) ..................................................................................................... 58 Baseline projection of heavy truck drivers supply. The data for Europe consists of EU 27, Norway and Iceland (ITF, 2017, p. 32) ............................ 75 Heavy commercial vehicle road freight demand (billion vehicle kilometers travelled). The data for Europe consists of EU 27, Norway and Iceland (ITF, 2017, p. 35) ........................................................................ 76 Heavy cargo trucks are parked in front of the headquarters of De Jong–Grauss Transport B.V. in Rotterdam, the Netherlands (picture is provided through the courtesy of De Jong–Grauss Transport B.V.) .............. 97
List of tables
Table 2. Table 3. Table 4. Table 5.
Levels of vehicle automation according to the SAE International J3016 standard as compared to BASt and NHTSA. Adapted from Janssen et al. (2015, p.6) after SAE (2014). ................................................... 21 Example systems at each automation level based on SAE J3016. Adapted by Shladover (2017, p.4).................................................................. 23 Overview of technology for driving automation systems (Lipson & Kurman, 2016; Gilbertsen, 2017) ................................................................... 25 Automated driving assistance functions in trucks (ATA, 2015) .................... 39 List of opportunities and barriers related to automated truck platooning in Europe ...................................................................................... 55
List of abbreviations ABS
Anti-Lock Braking System
European Automobile Manufacturers' Association
Adaptive Cruise Control
Advanced Driver Assistance System
AUTomotive Open System Architecture
German Federal Highway Research Institute
Cooperative Adaptive Cruise Control
Controller Area Network
Driver Assistive Truck Platooning
Forward Collision Warning
Global Navigation Satellite System
Global Positioning System
Heavy Goods Vehicle
Information and Communication Technology
Inertial Navigation System
International Road Transport Union
Lane Departure Warning
Long Heavy Vehicle
Lane Keeping Assist
National Highway Traffic Safety Administration of the United States
Original Equipment Manufacturer
Society of Automotive Engineers
Supply Chain Management
Traffic Jam Assist
Netherlands Organization for Applied Scientific Research
United Nations Economic Commission for Europe
Acknowledgements First, I would like to thank my thesis supervisor Ass. Prof. Mag. Dr. Petra StauferSteinnocher of the Institute for Economic Geography and GIScience (WGI) at the Vienna University for Economics and Business for her constructive feedback and rapid responses to all my questions. I am grateful to her for giving me the chance to choose a topic from the area of driving automation systems and steering me in the right direction throughout the research process. With her support, I was able to combine my personal interest in the automotive industry with a research in the field of supply chain management. Next, I would also like to thank my fellow graduate student Yvette for introducing me to Mr. Jan Verlaan from De Jong-Grauss Transport B.V. My gratitude goes to Mr. Verlaan for sharing a great deal of his knowledge about truck platooning and road freight transport in general with me. Without his contribution and the insightful conversations that we had, the haulier’s business perspective in this thesis would have been incomplete. Finally, as this thesis represents the last assignment of my studies at the Vienna University of Economics and Business, I would like to express my profound gratitude to my family for providing me with continuous support and encouragement throughout my years of study in Austria. Without them, this accomplishment would not have been possible. Thank you! Martin Dimitrov Roshkev Vienna, July 2017
Commercial road freight transport represents the backbone of trade and an important factor for economic growth in Europe. In addition, the European truck industry is the global leader in smart mobility and ICT (Information and Communication Technology). This flexible transport mode is used for a variety of goods and is fundamental for every integrated supply chain and transport system on the continent. Since the European community is largely dependent on trucks for daily distribution of goods, the potential to increase efficiency by utilizing recent advancements in the field of driving automation offers various economic and social opportunities. Yet, there are many barriers towards making the technology accessible in everyday business operations.
Motivation According to industry experts, the automotive sector will undergo more changes in
the upcoming ten years than it did in the preceding half of a century (Hetzner, 2016). Others go even further by claiming that what the humanity is going to see in the next two decades can surpass the automotive developments from the past one hundred years (NHTSA, 2013). With regards to industry changes, it is important to mention that nowadays automated vehicles are becoming some of the most intelligent devices. Using various sensors and powerful processors to sense and respond continuously to the surrounding environment, automated vehicles promise lower operating costs, less incidents, faster delivery and better resource utilization. The dream of having automated vehicles dates back to at least 1939, when General Motors presented their vision of 1960, featuring a system of intelligent highways and fully autonomously driving cars. While testing throughout the years did not lead to a production-mature status, recent advances in the field of robotics have the potential to finally change this. The vehicles are changing and so is their relationship between them and the drivers. The trend is going towards development of semi-automated vehicles and, in the future, fully automated ones. Hand in hand with those developments, the expectations of decreased pollution, less congestion, and a minimum of road accidents motivate the automotive industry to spend efforts in research and development of driving automation. As regards to road freight transport, in the upcoming years the term automated truck platooning would turn from being a peculiar idea found merely in professional journals to being part of the everyday logistics operations.
In the past few years, automated truck platooning, or sometimes just referred to as truck platooning, has been tested extensively in Europe. Specifically, the term refers to convoys of automated heavy transport trucks that are virtually connected to travel together on the road to save fuel costs through reduced wind-resistance. In truck platooning, there is a professional driver behind the wheel of the lead-truck part of a column of vehicles, while the rest of the trucks are following closely behind, operated by computers and not by their drivers. Each truck measures the distance to the truck in front of it and keeps it constant while performing adaptive longitudinal and lateral control (Fritz, 1999). Truck platooning is currently in development by various truck manufacturers worldwide and studies reveal findings ranging from fuel consumption reduction of just 5%, mentioned by Janssen et al. (2015), to up to 20%, measured during the SARTRE project (SARTRE, 2016). With regards to that, platooning is promising a huge cost saving potential for the road haulage businesses. Latest business forecasts state that if regulatory and technology issues are solved soon, 15% of the new cars might be able to drive fully autonomously by 2030 (McKinsey&Company, 2016a). Regarding the trucks themselves, given that the volume of freight transported on the road is expected to quadruple by 2050 (Martinez et al., 2014), automated driving could happen to be a key factor in re-shaping the logistics industry and setting new efficiency benchmarks for the supply chains of the future. Studies predict that if 90% of the vehicles were fully automated, the congestionrelated delays would go down by 60% on highways and 15% on suburban roads (Fagnant & Kockelman, 2015). Such development would be of a huge advantage for the logistics industry. However, from today’s point of view full driving automation is still a way off. On the other hand, the vision of platooning trucks exists for many years and since it does not require 100% automation, it may become reality long before fully autonomous trucks are allowed in regular traffic conditions. Moreover, prototypes have already proven the effectiveness of truck platooning in terms of better resource utilization. In the logistics industry, automated trucks would be able to gather and exchange data with other vehicles, but most importantly they will give drivers a chance to take hands off the wheel to rest or to occupy themselves with other work, while the truck moves by itself on the highway. In a world where the human error contributes to more than 90% of all automotive accidents (Singh, 2015), automated driving technology could not only have a positive economic impact for the industry, but would ultimately serve for safer roads by minimizing the number of deadly accidents. Although not fully automated, platooning trucks can contribute to reducing the number of crashes and fatalities on the roads by providing enhanced safety. The 10
combination of economic and social advantages is a motivating factor for automotive manufacturers and governments to invest efforts in developing platooning for public roads usage as a first step on the way to full truck automation. The advantages expected to be gained from platooning include lower transportation costs, decreased fuel consumption through enhanced aerodynamic efficiency, as well as better utilization of assets such as trucks, drivers and road infrastructure. Moreover, platooning can improve driving time and decrease the space used by trucks on motorways. In addition, reduced greenhouse gas emissions and congestion, as well as improved safety, promising less accidents and casualties, complement the image of platooning as a concept worth devoting academic research to. Additional motivation emerges from the significance of automated truck platooning for the SCM (Supply Chain Management) sector. According to industry reports, automated heavy trucks can decrease the total cost of forwarding providers by up to 50%. (McKinsey&Company, 2016b). Furthermore, drivers nowadays account for 30% to 40% of the total cost of operating a heavy commercial vehicle and even 60% in the case of small commercial vehicles (McKinsey&Company, 2016b). Many truck drivers might fear losing their jobs to robots, as a result of cutting costs. However, once automated driving trucks are ready for deployment, technology could solve the arising driver’s shortage problem that is expected to occur in the upcoming years. For instance, in the United Kingdom and Germany alone, the trucking industry is expected to be short 300,000 drivers over the next ten years (Oliver Wyman, 2016). Still, although there are advantages for the drivers, such as having more time to relax while travelling, as well as various safety benefits (Bergenhem et al., 2010), academic research reveals that some factors could have a negative effect on the drivers and could be a barrier towards successful implementation on public roads (Grahamslaw & Marsh, 2016). A variety of barriers are to be addressed and analyzed before trucks can be driven as part of automated convoys in everyday traffic conditions. Examples of such barriers include loss of situational awareness and unexpected changes in the driver’s behavior. In addition, the role of the human driver shifts from having total control over the vehicle to only supervising the driving task. Accordingly, this might lead to inattention, degradation of manual skills, and motion sickness (Cunningham & Regan, 2015). However, humans are not the only factor playing a significant role in the process of developing automated truck platooning. Legal obstacles also have the potential to slow down the implementation and to postpone the deployment of truck platoons on public roads. Anderson et al. (2014) have identified different liability issues and risks from government regulations, 11
and conclude that regulatory action can do more harm than good in the early stages of platooning development. Additionally, there are various issues which should be solved by the industry and governments together, ranging from engineering standards, through compliance, to enforcement, not to mention certification, insurance and allowed daily driver hours. Finally, a variety of technical issues are standing in the way of automated truck platooning. Public transportation infrastructure has not been designed for the purposes of truck platooning and might pose a barrier towards quick adoption of the concept. In addition, public acceptance and reaction of other traffic participants also have the potential to slow down the implementation of truck platooning. In either case, according to Janssen et al. (2015), the current economic political climate in the Netherlands and other EU (European Union) member states is very favorable to support large scale development of platooning. Since major vehicle authorities and truck manufacturers already have established continuous dialogue and are showing signs of mutual coordination, automated truck platooning has the chance to become part of the future of the European road freight transport sector.
II. Research objective Given the obvious relevance of automated truck platooning for future SCM operations, this thesis aims at exploring how this technology would affect the road freight transport sector in Europe. The research objective includes an investigation of opportunities and barriers linked to successful implementation of automated truck platooning with special focus on economic, technical, human and legal issues. The largest part of the research is based on a profound literature review, Internet research, and a review of industry reports. The literature review is complemented by an exchange of information with a road haulage company from the Netherlands. The research is concluded by an expert interview with a company representative having first-hand experience with the current developments around automated truck platooning. Overall, the thesis aims at answering the following research question:
Which are the opportunities and barriers of automated truck platooning towards successful deployment by the road freight transport industry on European public roads?
Since the professional literature comprises papers with a very specialized scope focusing mostly on the technical side of automated truck platooning (Alam et al., 2015; Bevly, et al., 2015; Liang et al., 2013), the need of an overview of the findings of those works has been identified. Therefore, this thesis is aiming at summarizing the opportunities and barriers of automated truck platooning related to road freight transport on a broader scale. Economic, technical, human and legal perspectives related to automated truck platooning are all part of the scope. The thesis aims to build a research based on a review of a broad range of literature. Additionally, the work was supplemented with consultations with an industry partner from the European road freight transport sector. However, it focuses mostly only on short-term developments related to automated driving technology. Developments related to driverless trucks are mentioned but are not central to this research. Due to the limited timeframe, this thesis does not guarantee to summarize every aspect of automated truck platooning. Although details of every identified opportunity and barrier are given as much as possible, the focus is on the big picture within the context of the road haulage industry. Practical limitations of this research include the short time period for conducting the research and the limited availability of industry experts. The thesis can serve as a basis for future research specified in one of the focus areas. In addition, it can help hauliers, shippers and logistic service providers to understand how automated truck platooning works and to learn which opportunities and barriers are expected on the way to successful implementation.
III. Thesis structure The chapters of this master’s thesis are logically structured to narrow down from a broader perspective to the specific issues of automated truck platooning. The first chapter serves as an introduction including motivation and a presentation of the research objective. What follows is an overview of the market and the technology of automated driving systems and automated truck platooning. Chapter 2 is based on a literature review and Internet research. It starts with a subchapter presenting an overview of the road freight transport sector in Europe and later explores the driving automation systems in general. Then, a subchapter with an elaboration on definitions related to automated driving is followed by another focusing merely on truck platooning technology in the context of automated driving. After providing an overview of the standard technology, the stakeholders and the timeline of expected introduction are set out in the subsequent subchapter. The last
subchapter is dedicated to a retrospective overview of automated truck platooning projects and road tests which had significant impact in the past years. Although Europe is the special focus of the thesis, relevant experience from other parts of the world, especially North America, is also being mentioned. Then, chapter 3 represents the final focus of this research and considers the opportunities and barriers of automated truck platooning. The findings are based on a literature review and Internet research. Chapter 3 is divided into four subchapters with foci related to economic, technical, human, and legal aspects. Each of the subchapters includes an overview of the most significant opportunities and barriers related to the respective foci. Chapter 4 is dedicated to consultation with an industry professional and includes subchapters including the description of the methodology, the interview, as well as the outcome. The findings of the interview are meant to underline the importance of findings collected during the literature review. In addition, the discussion with the industry expert helps to point out possible directions for future academic research seen through the prism of the European road haulage industry. Finally, the conclusions made in chapter 5 are based on the findings presented throughout the thesis. Considerations about possible future research are marking the end of the research.
Market and technology overview
This chapter provides an overview of the road freight transport market and explains automated driving technology in its context. The foci range from the freight transport industry in Europe to a review of tests with automated driving trucks. Chapter 2 is divided into following subchapters:
Road freight transport
Driving automation systems
Automated truck platooning
Stakeholders and timeline
Projects and road tests
2.1 Road freight transport Ross (1998) states that a SCM process is the way a physical business moves goods and services to bring them to the market. As a part of this process, logistics includes activities ranging from warehousing to transportation across the supply chain. Being an integral part of logistics, road freight transport represents a large portion of Europe’s total goods transport. According to Eurostat, road freight transport holds the largest share of the total European inland transport. In 2014, around 75% of the freight or, in other words, more than 1,600 billion ton-kilometers were transported over roads (Eurostat, 2017). The importance of this sector to the European Union’s economy is also highlighted by the high number of international transports: around 34% of the total road freight is moved across borders using heavy goods vehicles (Wolters, 2016). To put it briefly, one third of the road freight volume is potentially subject to optimization through the introduction of automated truck platooning. Furthermore, technical innovations have long been an essential part of the European automotive industry which, in turn, is among the global leaders in the truck segment. The automotive industry, including trucks and buses, plays a key role for the economy of Europe. ACEA (2017a) (The European Automobile Manufacturers’ Association) claims that the vehicles produced on the continent are among the safest and most environmentally friendly ones in the world. Among others, the European manufacturers
excel with clean production processes and sustainable usage of natural resources. Furthermore, the road transport industry is contributing greatly to the European Union’s economic growth and accounts for more than 5% of its gross domestic product (Eckhardt, 2016a). Moreover, directly and indirectly, the sector employs over twelve million people, or a total of 5.6% of EU’s manpower (European Commission, 2017). The automotive industry not only operates a vast amount of production sites and generates revenue for the continent, but also greatly stimulates innovation. According to ACEA (2017a), the automotive industry invests over 50 billion Euro annually in research and development. Additionally, in 2016 the European Patent Office granted over 8,000 patents to automotive manufacturers alone (ACEA, 2017a). At the same time, academia has also been largely engaged in conducting research on new technologies for the automotive industry. In total, 18% of the world’s units of trucks, vans and buses are produced in the European Union (ACEA, 2017e). Speaking of commercial vehicles only, 23 million new units are delivered globally every year (ITF, 2017). This fact reveals the significance of the global truck market and the potential profits. According to ACEA (2017e), the trucks made by European brands are among the most technologically-advanced and safest on the market. Moreover, by transporting goods, they represent an efficient way of linking businesses and customers. As a result, ACEA (2017e) claims that 90% of the freight in Europe is carried by trucks. In addition, ACEA (2017e) states that the newly introduced EURO VI standard has decreased the allowed regulated emissions by 98% as compared to the beginning of the 1990s. Research in development, on the other hand, led to technical advancements contributing to a 60%-reduction of CO2 emissions as opposed to 1965. Despite the indisputable improvements in terms of efficiency achieved over the last decades, there is still more to be done in order to make cargo trucks safer, more efficient and more environmentally friendly. The platooning concept has a chance to further increase the CO2 reduction by achieving fuel savings that would otherwise not be possible with current technology. Considering this, it is not surprising that major innovations in the automotive industry require substantial financial investment. According to EUCAR (2017) (European Council for Automotive Research and Development) the European vehicle manufacturers represent the biggest private investors in research and development on the continent, and every year they invest in it more than 4% of their total turnover. In contrast to the truck manufacturers, however, the road freight carriers do not possess the financial power to innovate constantly. Therefore, they are only ready to invest, if the technology proves to really yield economic benefits. On the other hand, the industry is experiencing a constant 16
growth and according to ACEA (2017b), only between 2010 and 2013 the ton-kilometers performance of road cargo transport achieved a growth of 14%. The EU transportation sector has currently more than 2.4 million employees and 6.4 million trucks at command. In total, the European freight transportation and distribution business is estimated to have an early value of 250 billion Euro. Tsugawa et al. (2016) mention an annual growth of 5% in the European freight transport sector, and a total growth of 50% between 2000 and 2020. This volume is concentrated on the road network and due to the short distances cannot always be compensated by rail cargo. In fact, 85% of the goods transported with trucks are not moved on distances longer than 150 kilometers (ACEA, 2017e). Consequently, in the upcoming decades the European roadway network could be overloaded and that would lead to increased congestion and transportations costs, as well as environmental issues with further risks of increasing economic and social risks. Therefore, as long as road transport is concerned, recent technical advancements such as automated driving are seen as one of the right tools to cope with the upcoming challenges. Road transportation is part of the main scope of freight forwarders, hauliers and logistics service providers. These businesses could benefit the most from automated truck platooning and a recent study by Janssen et al. (2015) proves the potential positive business case. Moreover, companies would not hesitate to invest in platooning technology, if the manufacturers manage to demonstrate that platooning is safe and leads to financial benefits. In contrast to passenger cars, which spend most of their time parked and barely utilized (Lipson & Kurman, 2016), investment in additional driving automation systems can prove to be beneficial to road freight transport due to the high number of kilometers a commercial truck is covering annually. To support this, researchers also believe that the potential high utilization and cost-saving opportunity of platooning could find early adopters in the face of the transportation businesses (Janssen et al., 2015). Before going into more detail, however, an important clarification should be made: although automated truck platooning could be related to any kind of trucks, this thesis focuses primarily on those transportation trucks used mostly on the European continent. These are classified as category ‘N’ according to the European classification scheme. Category ‘N’ means that they are “power-driven vehicles having at least four wheels and used for the carriage of goods” (UNECE, 2007). Of interest for this master’s thesis are the vehicles of category ‘N3’, or such with a maximum weight exceeding twelve tons. These heavy commercial trucks are often referred to as HGV (Heavy Goods Vehicle).
From an SCM perspective, truck platooning could have a significant impact on the profit margins achieved by hauliers. Moreover, when interoperability among different brands is achieved, platooning can boost the level of collaboration and information sharing. In their study on truck platooning, Janssen et al. (2015) point out the need of introducing a new business concept, namely that of a platooning service provider. Its role in the supply chain would be to bear the responsibility for helping trucks from different fleets to find and connect to a platoon and to manage schedules of platoons. To ensure the successful future of platooning, the researchers emphasize the need of mutual collaboration of all stakeholders – from national regulators to hauliers. The positive business case for the transportation companies, however, could be the leading factor of implementing truck platooning beyond the doors of closed test tracks. In addition, research conducted by the American Transportation Research Institute reveals that fuel costs and driver remuneration still account for the biggest part of the total truck operating costs (Torrey et al., 2014). If deployment of fully automated trucks proves to be possible on public roads, fuel costs could be reduced significantly and driver cost could vanish completely. However, from today’s point of view full driving automation on public roads, and especially with mixed traffic, seems to be a long way off. How to understand the term driving automation in detail, as well as how it refers to automated truck platooning is subject to investigation in the next chapter.
2.2 Driving automation systems The significance of road freight transport for Europe’s economy has been illustrated with facts in chapter 2.1. Nevertheless, to gain better understanding of automated truck platooning, one should also become acquainted with the basic principles of driving automation systems. The following chapters outline the underlying industry standards for vehicle automation, the required technical equipment and sensors, as well as the functionality of automated truck platooning in the following order:
I. II. III.
Industry classifications Technological framework On the way to fully automated driving
Nowadays, terms like ‘autonomous’, ‘self-driving’, ‘robotic’ and ‘unmanned’ are often used synonymously by mass media. While the press and the general public do not necessarily distinguish between them and sometimes misinterpret their meaning, there are industry standards that set exact definitions. In the first place, the J3016 standard of the Society of Automotive Engineers, known today as SAE International, will be reviewed in order to avoid imprecise usage of various terms and possible misinterpretation throughout the document. To start with the act of driving, or the so called ‘dynamic driving task’, one should interpret it as the act of conducting operational and tactical tasks by a human in real time, with the aim of operating a vehicle on a public road with surrounding traffic (SAE, 2014). Consequently, driving automation systems are those which conduct the automated task of driving a vehicle (Shladover, 2017). In some cases, however, the system is not responsible for the entire driving task but only for a portion of it. This is the prevailing case in lower levels of driving automation which will be subject to review later on in this chapter. Secondly, the term ‘autonomous’ implies that a vehicle can perform all tasks completely on its own and does not rely on communication with the surrounding environment by using V2V (vehicle-tovehicle) or V2I (vehicle-to-infrastructure) communication systems. This clarification contradicts the nature of most of today’s driving automation systems which rely on data exchange with vehicles, satellites or other devices and, consequently, explains why ‘autonomous’ is not always the most appropriate wording. The case is similar with the terms ‘driverless’ and ‘unmanned’ – those point out at vehicles without a human driver in the driver’s seat and might even mean a vehicle without a cockpit at all. The latter also holds true for the term ‘robotic’ – a word which is vague in the context of automated driving. It could refer to any system that relies on automation – including vehicle applications for automation like ABS (Anti-Lock Braking System), which has been available in series production cars for decades. After all, when speaking about vehicles not equipped with systems to control 100% of the dynamic driving task, SAE (2014) advises to refer to a ‘vehicle equipped with a driving automation system’ or to an ‘automated vehicle’. Moreover, SAE (2014) does not recommend the usage of ‘self-driving’, ‘driverless’ and ‘robotic’. To clarify, ‘self-driving’ points out to the eventual full extent of vehicle automation or the point in time when vehicles would not need any human action to drive themselves and to take decisions in real traffic environment. However, the achievement of such automation levels 19
for commercial usage still needs substantial amount of technological research. Nonetheless, there is no industry definition for platooning trucks in special. Therefore, throughout this paper the terms ‘automated truck platooning’, ‘truck platooning’ and ‘platooning’ will be used interchangeably and, at the same time, they will refer to HGVs only. Driving automation systems in the context of platooning will be referred to as systems which can sense the surrounding environment, communicate with other road entities and can take navigation decisions to drive themselves without human interaction. Additional key definitions standardized by automotive industry standards are the ‘request to intervene’ and, as has been mentioned before, the ‘dynamic driving task’. The first describes a message from the driving automation system to the human driver that they should resume driving, while the latter refers to the operational and tactical facets of the driving task, except the strategic ones. To explain, operational activities include “[…] steering, braking, accelerating, [and] monitoring the vehicle and roadway […]”, tactical tasks refer to “[…] responding to events, determining when to change lanes, turn, use signals […]”, and strategic are those that are “[…] determining destinations and waypoints […]” (SAE, 2014). Since the levels of driving automation will be mentioned often throughout the paper, it is important to become acquainted with the industry classifications. Among the first official authority to standardize the matter was the NHTSA (United States National Highway Traffic Safety Administration). The agency develops and enforces safety regulations and standards for vehicle equipment. In addition, it issues regulations limiting the environmental impact of motor vehicles. Due to the increasing advances of automated driving technology in the past few years and the expected change in the vehicles in the near future, NHTSA (2013) issued a statement to address safety regulations regarding future mobility on public roads. In this statement, an automated vehicle is defined as one that possesses the ability to conduct a portion of the safety-critical control functions without the need of driver’s action. In addition to the classification issued by NHTSA, the BASt (German abbreviation for German Federal Highway Research Institute) published a research report on the legal consequences of increased vehicle automation and set standards for the nomenclature, as well as an investigation of the theoretical impact of driving automation systems (Gasser, et al., 2013). More importantly, in 2014 SAE (2014) issued a recommended practice document with definitions and taxonomy to standardize the levels of driving automation. SAE focuses on clarifying the human driver’s role during the usage of driving automation systems and gives recommendations regarding specifications and technical requirements. The great value of SAE’s document lies in its consistency with the industry practice and existing documents 20
issued by the other two organizations. Moreover, it aims at reaching a broader audience outside the industry world and facilitates the understanding and requirements of driving automation systems towards the general public.
Levels of vehicle automation according to the SAE International J3016 standard as compared to BASt and NHTSA. Adapted from Janssen et al. (2015, p.6) after SAE (2014).
Nevertheless, despite the overlap in focus between the documents, there are some differences in the assignment of automation levels by each of the organizations. As seen in Table 1, both NHTSA and BASt set five automation levels for vehicles ranging from the
driver being fully in charge (level 0) to full automation where the driver does not bear any responsibility (level 4) (NHTSA, 2013; Gasser, et al., 2013). While the NHTSA focuses more on the control over safety-critical functions to differentiate between the levels, BASt, on the other hand, refers to categorization of functions for automated driving and sets the standard according to the driver’s perspective and the actions expected at any level. BASt also gives examples of systems for every automation level, with some of them already available in today’s high-end production cars as separate features. Besides NHTSA and BASt, SAE International’s J3016 standard defines six levels of driving automation which are commonly referred to as ‘SAE levels’ (SAE, 2014). As can be retrieved from Table 1, SAE puts the focus on the role split between human and system. In contrast to the German and US vehicle authorities, the SAE organization creates an additional automation level as compared to the classification in previous standards. This is possible by introducing more detail to all the levels ranging from three to five. As a result, from SAE level 0 until SAE level 2, the human driver is responsible for monitoring the driving automation system’s performance. From then on, the system is taking over the monitoring process. Thus, SAE level 3 already refers to systems capable of handling acceleration, braking and steering simultaneously, under certain limited conditions and requiring the driver to be able to take control at any given time. SAE level 4 depicts the same scenario, however, the driver is not obliged to react, if requested to intervene by the system. Finally, SAE level 5 represents a state where the driver is not needed at all and the system is in control of any function which a human is normally responsible for. To facilitate understanding of the SAE levels, Figure 1 provides an alternative representation of the scheme depicted in Table 1. The organizations involved in setting standards regarding automated driving are not limited to the three mentioned before. However, the standards of SAE, NHTSA and BASt are those largely adopted by the industry. In fact, SAE, being an international industry union, is cited the most. Therefore, the levels of automation set in the SAE J3016 standard are used as a reference in this thesis. Nowadays, car manufacturers and motoring journalists also use the SAE J3016 standard to assign automation levels to vehicles equipped with automated driving functions. Although the broad audience often refers only to passenger cars when speaking about driving automation systems, the functional definitions are the same for other road vehicles including heavy cargo trucks. Further example of how SAE levels map to today’s driver assistance systems in road vehicles is presented in Table 2.
To sum up, autonomous vehicles are those that can operate automatically without the need to connect to other vehicles and thus do not have V2V communication tools. In addition, autonomous vehicles can also be regarded as such without a human driver at all. On the other hand, automated vehicles can be autonomous if assigned SAE level 5, but can also refer to lower SAE levels – for instance, to cooperative driving systems like those of SAE level 1 and 2, where a human is responsible for the dynamic driving task. Another clarification with importance to automated truck platooning are the axes of control of a vehicle. Vehicles equipped with driving automation systems can, but do not necessarily have to be able to maintain both lateral and longitudinal motion control in an automated way (SAE, 2014). Lateral control describes the ability of a vehicle to determine its own position within the road lanes and to steer and brake by itself to keep the desired course. Longitudinal control, on the other hand, refers to the activity of sensing the front-moving vehicle. In addition, it refers to accelerating and/or braking to keep a constant distance between both vehicles, as well as to maintain the desired speed.
Adaptive Cruise Control OR Lane Keeping Assistance
Must drive other functions and monitor driving environment
Adaptive Cruise Control AND Lane Keeping Assistance Traffic Jam Assist Parking with external supervision
Must monitor driving environment (system nags driver to try to ensure it)
Traffic Jam Pilot
May read a book, text, or web surf, but be prepared to intervene when needed
Highway driving pilot Closed campus "driverless" shuttle "Driverless" valet parking in garage
May sleep, and the system can revert to minimum risk condition if needed
Ubiquitous automated taxi Ubiquitous car-share repositioning
The system can operate anywhere with no driver needed
Example systems at each automation level based on SAE J3016. Adapted by Shladover (2017, p.4)
It is important to understand SAE’s classification scheme because some terms refer only to certain levels of automation. Moreover, automated lateral and longitudinal control are not among the capabilities of every driving automation system. In this regard, while speaking about automated truck platooning in the following chapters, the term ‘supervising’ will be used in the context of SAE level 1 and 2 systems. Those systems require that the driver can monitor the performance of the vehicle and can proactively take corrective actions, such as
braking or steering, if the system fails to deliver the desired outcome. The trucks subject to investigation will be referred to as ‘automated’ or as possessing driving automation systems covering some aspects of the dynamic driving task, but not to as being autonomous. The term ‘autonomous’ will be used only in relation to vehicles with or without a human in the cabin that can be assigned SAE level 5 and can perform the full range of the dynamic driving task automatically and without the need for human supervision.
Figure 1. Another view of SAE’s levels of automation – responsibilities of human and system (Eckhardt, 2016, p. 67)
In order to maintain higher SAE levels, the vehicles require state-of-the-art software and hardware sensor technology. Nowadays, there are different hardware and software setups enabling automated driving. In short and as summarized in Table 3, a driving automation system comprises a set of radar, lidar, vision-based camera, ultrasonic sensors, GNSS (Global Navigation Satellite System), INS (Inertial Navigation System), V2X communication and a database of high-definition maps (Tsugawa et al., 2016, Lipson & Kurman, 2016). Before digging deeper into the technological framework, it is important to
note that the progress motivators behind the recent boom of automated vehicle technology are the simultaneously happening advances in several technical fields.
Purpose Stands for sensor for Radio Detection and Ranging. Uses electromagnetic radio waves to detect objects and their distance, size, angle and velocity. Works in every condition and sees behind obstacles. Lidars can be both short and long-range.
Stands for sensor for Light Detection and Ranging. Uses laser light for object detection, scans the environment in each direction and creates a 3D profile of the surroundings. Measures distance in a plane with higher resolution than a radar.
The backbone of computer vision. Used for perception of the surrounding environment in color: for recognition of traffic lights, brake lights, turn signals, for reading traffic signs and markings, for front and rear obstacle detection, as well as collision warning. Modern models offer high resolution and high-detection with 360 degrees coverage achieved by operation of multiple cameras (stereo vision) able to reconstruct a 3D environment. Infrared cameras with night-vision capability detect pedestrians and animals.
Calculates distance based on time of flight of a sound wave. Much more affected by temperature and less accurate than a radar. Used for vehicle parking assistance.
Stands for Global Navigation Satellite System. A standard term for a satellite navigation system which provides geo-spatial positioning with global access to satellites from different systems (e.g. GPS, GLONASS). GNSS systems deliver an accurate position and help to estimate speed and direction of movement, as well as to navigate vehicles.
Stands for Inertial Navigation System. Monitors how a vehicle is dynamically changing its movement using an accelerometer (motion sensor) and gyroscope (rotation sensor). Supports the GNSS with accurate information about the vehicle's kinematics. Orientates the vehicle in the absence of GPS signal, e.g. in tunnels.
Stands for Vehicle-to-Everything communication and means transferring information from a vehicle to surrounding entities and back. Mostly mentioned in the context of automated driving systems are V2V (Vehicle-to-Vehicle) and V2I communication (Vehicle-to-Infrastructure). V2X communication is based on WLAN technology for wireless local area networking. The IEEE 802.11p standard was developed to support Intelligent Transportation Systems (ITS) applications.
Stands for High-Definition Digital Map. Highly-detailed maps for automated driving vehicles include data from various sensors such as GPS, cameras and lidar. The map includes an accurate roadway profile, curvature and terrain. In the future, cloud-based solutions are expected to be updated continuously with real-time data gathered by vehicles equipped with advanced driving automation systems.
Overview of technology for driving automation systems (Lipson & Kurman, 2016; Gilbertsen, 2017)
First, modern vehicles are increasingly being equipped with ADAS (Advanced Driver Assistance Systems). For instance, crash avoidance systems warn the driver in case the
system has predicted an accident. In brief, such vehicles can take limited control over safetyrelated activities such as braking and steering. However, these systems work only in limited situations and can be described as event-triggered. In fact, automation aims at replacing the human driver over a longer period of time with the help of sensor technology which monitors the driving environment constantly and software that ensure safe system behavior. When speaking of automated truck platooning in the context of ADAS, it is worth noting that radarbased features like CACC (Cooperative Adaptive Cruise Control) are available in commercial trucks since 2008 and are among the most important applications enabling driving automation in the road haulage sector. According to trucking industry associations, there are more than 100,000 trucks equipped with those features already on the roads (ATA, 2015). The second driver behind the trend of automated driving advancements is V2X (Vehicle-to-everything1) technology that is used for communication between vehicles and other objects of the road infrastructure (Lipson & Kurman, 2016). By supporting real-time data exchange, V2X enables the connection between different users and elements on and off the road. Finally, the third driver behind automaton are the advancements in sensor technology and cameras which are proliferating in the recent years. Seeing that, one can remember what Gordon Moore predicted more than five decades ago. Back in 1965, the cofounder of Intel Corporation stated that the number of transistors in an integrated circuit will double every two years (Schaller, 1997). In other words, the Moore’s law means that hightechnology products will be constantly getting faster and better, while the prices of hardware components will be going down. Because this is really happening, this trend is probably the most significant one of all three motivators mentioned so far. From a technical and economic point of view it is greatly enabling the advancements in the field of driving automation systems. As has been mentioned before, driving automation systems are comprised both of software and hardware. The primary feature of vehicles using this kind of technology is their ability to detect the surroundings using sensors ranging from radar to video cameras. Those features are being increasingly used in high-end passenger cars produced in the past decade. Nevertheless, they have their roots in robotics engineering. According to academic experts, the scientific field of robotics defines two types of low-level and high-level controls (Lipson & Kurman, 2016). Low-level controls comprise internal systems like braking, acceleration and steering, while high-level controls are responsible for more general and long-term tasks 1
V2X could represent both V2V and V2I communication
like navigation. Meanwhile, automation of low-level controls is being actively implemented by the automotive industry for years. Systems such as ABS, cruise control, and automated parking can be found in many of today’s passenger cars. Moreover, high-level controls that govern long-term navigation tasks are even more distributed and could be found in sectors ranging from military to civil aviation and maritime transport. A decade ago, navigation has been optional and rather exclusive feature reserved for high-end passenger cars. Nowadays, navigation is standard equipment in a great number of cars and trucks including the middle and lower-class vehicles. Moreover, it is available at much lower prices compared to the past. With regards to navigation systems, it is worth to briefly mention how they work. Literature reveals that the backbone of those systems mostly involves complex algorithms that compute the shortest path. The base approach includes the usage of the classic A* and Dijkstra algorithms used to set the optimal path between two nodes on a network (Vasseur et al., 1992). The calculation of the optimal path in modern navigation systems is complemented by real-time data received by GPS (Global Positioning System) or GNSS devices and exchanged with other vehicles on the road via V2V communication. Accurate navigation systems are only one piece of the puzzle of driving automation. Although the underlying principles are the same as for partly-automated driving vehicles, full automation is not expected to hit the market before 2050 (Eckhardt, 2016a), and this forecast is still highly uncertain. To get an overall impression of technology used in driving automation systems, a short summary is provided in Table 3. In the case of higher SAE levels, vehicles find their way using GPS signal and follow HD maps without the need to communicate with the surrounding infrastructure. Such vehicles are equipped with additional sensors like digital cameras, lidar, radar, ultrasonic sensors, GNSS and more. Some of them have similar functionality but are used simultaneously to secure redundancy and robustness of the entire system. The different sensors can also be reviewed from the point of view of the axes of rotation. First, lateral control is achieved by cameras mounted on the front and the rear of the vehicle to provide 360-degree view. They detect the lane markers and measure the distance and angle of the vehicle compared to the markers. In favor of assuring sensor redundancy, both camera and lidar are used for lateral control. In the case of automated truck platooning, each vehicle controls its lateral position independently (Yoshida et al., 2010). Next, for longitudinal control, radar and lidar are required to detect the other road vehicles and to maintain the desired inter-vehicle gap. Longitudinal control governs both speed and gap length but is not maintained self-reliantly – it depends on the other trucks in the platoon (Sugimachi et al., 2013). At the end, V2V communication is used 27
to ensure real-time data exchange between the trucks including speed, braking, position, acceleration etc.
III. On the way to fully automated driving In the future, fully automated road vehicles would rely on an approach called ‘fleet learning’. Eventually, this approach will ensure that the more vehicles are gathering information in real-time, the more accurate the shared map database and the better the surround recognition capabilities will be (Lipson & Kurman, 2016). Additionally, fleet learning also applies to the recognition of traffic signs, road markings, pedestrians and other vehicles. However, fleet learning should be distinguished from other approaches. Specifically, some of the most advanced software solutions in today’s driving automation systems already rely on artificial intelligence: a rule-based data-driven approach called ‘machine learning’ (Negnetievsky, 2005). Machine learning has its roots in computer science and has helped researchers to develop computer vision used widely together with cameras and HD maps to make vehicles gradually learn how to properly sense the surroundings and how to take decisions on their own (Stein, 2006). According to Mobileye (2017), the backbone of current development of autonomous driving and ADAS is the usage of computer vision. Camera sensors with 360degree coverage ensure that the driving automation system operates with perception close to that of a human being. An example of computer vision is provided in Figure 2.A. The snapshot shows how a high-detection camera recognizes the surrounding objects as well as their distance and velocity, and classifies them as vehicles, pedestrians, cyclists etc. In addition, camera sensors can also detect traffic lights and read traffic signs (see Figure 2.B). Although some manufacturers share the opinion that camera sensors alone are sufficient for autonomous driving to become reality, the automotive industry largely relies on additional sensors such as radar and lidar, in order to secure redundancy and robust functioning of the driving automation system. Similar to the advancements in sensor technology, it is noteworthy that the times of paper maps are over and nowadays, in-vehicle navigation systems represent standard equipment. Next to computer vision that tackles real-time traffic situations, constantly updated HD maps are crucial for the vehicle’s navigation. Another important factor is that HD maps are different from standard maps in that they include both large, 3D-formatted geographical features such as mountains, as well as small topographical details like sidewalks, road signs and traffic lights.
Figure 2. A. Vehicle and pedestrian detection; B. Traffic light detection (Mobileye, 2017)
As shown in Figure 3.A, critical parts of the traffic environment like road intersections are depicted in 3D detail to minimize the potential of accidents. To guarantee that maps are as up-to-date as possible, machine learning algorithms could be integrated in the vehicle cameras to allow the sensing and recognition of the environment while the vehicles learn to take decisions by themselves. This process is supported by calculating driving paths based on the road infrastructure and the surrounding vehicles. To facilitate fleet learning, the data gathered during driving is processed to a common database and integrated into an HD map, available to every vehicle which shares the same system. Figure 3.B represents a car’s visual understanding about where it is in the world at a moment. The road and the trajectories of other road users are depicted on different layers with distinct colors. Based on the visual perception of the surroundings, the car predicts the behavior of the other vehicles and makes a decision how to adapt its lateral and longitudinal speed and positioning.
Figure 3. A. Real-time updating HD road map (HERE, 2017); B. HD road map and driving patterns (Urmson, 2015)
Nowadays, testing vehicles fitted with high-precision GPS systems are able to collect up to 100 gigabytes of mapping data a day (The Economist, 2016), including the car’s
latitude, longitude and elevation ten times over. The data is utilized by digital cartographers to build a high-definition 3D map image of the vehicle’s route that would be used by OEMs (Original Equipment Manufacturer) in future series vehicles manufactured with driving automation systems on board. According to the press, citing automotive professionals, commercial GPS systems are accurate only to around five meters which would make automated driving risky. However, companies like TomTom, Google and HERE, claim to achieve up to 10-20 centimeters of accuracy by merging multiple layers of data on a single digital map (The Economist, 2016).
Figure 4. A. Automated guided vehicles for horizontal transportation (Konecranes, 2017); B. Automated heavy truck platoon arriving at the Port of Rotterdam during the 2016 EU Truck Platooning Challenge (EU Truck Platooning Challenge, 2016)
Furthermore, there are two approaches towards future development of fully autonomous driving. The first one is to create and market an entirely autonomous robotic vehicle, even without steering wheel and pedals. Companies, such as Google believe that intermediate steps including humans and machines with mixed responsibilities regarding the dynamic driving task are too dangerous. Thus, Google is aiming at launching a completely automated vehicle for passenger transportation once the technology and regulations allow it (Lipson & Kurman, 2016). The issue of mixed responsibilities between computer and human are investigated in the light of automated truck platooning in chapter 3. As of today, fully autonomous trucks are successfully being deployed in various scenarios under the term AGV (Automated Guided Vehicles). They are mostly used for repeating activities such as transport of materials and heavy objects in warehouses, production facilities, container terminals and especially in the mining industry (Vis, 2006; ITF, 2017).
Figure 5. The expected transition from ADAS to entirely automated driving (Renesas, 2017)
An example of AGV-suitable environment are the container terminals in the Port of Rotterdam, where the horizontal transportation of containers is conducted with robotized trucks (Figure 4.A). However, the reader should distinguish between AGVs and HGVs equipped with driving automation systems (Figure 4.B). While the first are deployed in closed environments and exposed to limited external risks, the latter operate on public roads – a highly unpredictable traffic environment where driving automation meets more challenges. Contrary to Google’s approach, the majority of the automotive manufacturers share the opinion that automated driving technology has to develop incrementally, so that over time the number of automated features increases in parallel to the technology advancements, until it eventually reaches full autonomy. The same applies to heavy duty trucks for road freight transport which cannot be automated instantly due to various factors. Indeed, there is a substantial difference between Google’s vision and that of traditional automotive manufacturers. Google’s car, like some AGV’s, does not share or communicate any data with other vehicles. In contrast, this activity is the basis for automated driving technologies such as truck platooning. In other words, traditional car OEMs think that the future development should go all the way from low levels of cooperative automated driving to full autonomy. This is the philosophy of companies like BMW, Tesla, Audi and others, who already offer a variety of driving automation features in their passenger cars (Jaynes, 2016).
Figure 6. Example of a prototype road car with sensor technology for autonomous driving and the corresponding customer functions (Audi, 2014)
An example of a prototype vehicle offering ADAS functionality is depicted in Figure 6. The different sensors are installed in- and outside on a chassis of an existing series model. Normally, different features offer automation for activities ranging from low speed limited traffic jam assistance, through highway traffic assistance, up to pedestrian/vehicle detection and avoidance systems (Jiang et al., 2015). To sum up, both the incremental and the fully autonomous approach have positive and negative aspects. However, detailed analysis will not be subject to this thesis. One further clarification should be made regarding the distinction between ADAS and autonomous driving. According to the vision technology developer Mobileye, while autonomous driving refers to a system that is in full control of the dynamic driving task and human intervention is not required to operate the vehicle, ADAS represent various functions that support the human in driving or simply automate a small portion of the dynamic driving task (Mobileye, 2017). In addition, ADAS can be both passive and active ones. Passive ADAS warn the driver in the case of any danger, for instance, when the vehicle unintentionally leaves the lane or is on course to collide with another vehicle. In this situation, the driver is responsible for taking corrective actions to prevent an accident. Such functions are known as LDW (Lane Departure Warning) and FCW (Forward Collision Warning). On the other hand, active safety systems are acting autonomously and comprise emergency braking, lane keeping or aid in traffic jams. This means that in certain situations the steering and breaking action is taken over by the system. Exemplary functions include ACC (Adaptive Cruise Control), LKA (Lane Keeping Assist), LC (Lane Centering), and TJA (Traffic Jam Assist) – a mixture of the LKA and LC applications (Mobileye, 2017). The majority of those functions are assigned SAE level 1 and are important in the context of automated truck platooning. An example of how the functions are expected to evolve from ADAS to automated driving is provided in Figure 5. According to the American Trucking Organization, in 2015 some 100,000 trucks in the USA were already equipped with an ACC function (ATA, 2015). Although no official data for Europe could be retrieved, the number of trucks equipped with ACC is believed to be rather high, given the amount of European truck manufacturers offering latest technical developments for their heavy commercial vehicles. Furthermore, ACC is the backbone of automated truck platooning where the system is responsible for the longitudinal control and for keeping a safety distance towards the front-moving vehicle. In the context of trucking, CACC is sometimes even used as a synonym for platooning. Next, DATP, short for Driver Assistive Truck Platooning, is yet another widespread abbreviation that is used 33
interchangeably and is popular mainly in North American literature (Humphreys et al., 2016). Other definitions provided by the Auburn University include the term ‘heavy truck cooperative adaptive cruise control’ (Bevly, et al., 2015). Apart from those mentioned before, several additional functions can also be encountered in the context of automated truck platooning. More about this and a special focus on automated truck platooning is subject to the following chapter.
2.3 Automated truck platooning After elaborating on the driving automation systems and separating autonomous from partly automated vehicles, the focus in this and every following chapter will be devoted to automated truck platooning. As has been mentioned before, the term automated truck platooning refers to fleets of automated vehicles, mostly columns of heavy cargo trucks that are virtually connected to travel together to save fuel costs by reducing wind-resistance. Platooning is gaining attractiveness largely due to its efficiency and cost saving potential for transportation companies. Experiments with platoons of two, three or more heavy trucks have shown the effectiveness of this approach in achieving energy saving thanks to the short gaps between the vehicles (Tsugawa et al., 2016). Nevertheless, due to the large number of tests conducted only with combinations of two trucks, the likelihood that first deployment on public roads would happen in formation of two trucks is high. Moreover, although a platoon of three and more trucks achieves higher overall fuel savings, the leading truck experiences higher loss of time until the following trucks have connected themselves, given that the platoon is not scheduled in advance (Bakermans, 2016).
Figure 7. Truck platoon with radar and lidar technology and wireless communication (Janssen et al., 2015, p. 7)
Next, Janssen et al. (2015) contend that most of the European drivers are familiarized with tractor-trailer combinations with a length of 18.75 meters, and especially with long
vehicles with a maximum length of 25 meters. Considering a platoon with two 18.75-meters long vehicles and the distance between them, the resulting formation of 44 meters is already long enough to cause confusion among other traffic participants. For this reason, some industry expects share the opinion that at the start, only two-truck platoons will be operated (Janssen et al., 2015). Even so, one should keep in mind that platooning is possible with virtually unlimited number of trucks and cars in a variety of combinations. The only prerequisite is that the vehicles are equipped with the required hardware and software setup. In addition, in Europe the heavy trucks are electronically limited to a certain top speed and this limitation could naturally facilitate grouping in platoons on the highway (Ploeg et al., 2011).
Figure 8. Wind speed around platooning trucks calculated with computational fluid dynamics (Alam, 2014, p.46)
Then, the main features needed for automated truck platooning are a combination of sensor technology, ADAS and communication units like radio connection and Wi-Fi technology. Figure 7 provides a simplified view of a two-truck platoon and the basic technical concept behind it. In contrast to the vision of fully automated trucks, automated truck platooning includes a professional driver behind the wheel of the first truck in a column of trucks (leading vehicle). The rest of the trucks are driving closely behind, operated by computers and not by their drivers (following vehicles). Every truck measures the distance to the truck in front and keeps it constant while adapting the speed and the direction if needed. According to Janssen et al. (2015), the maximum proximity of two trucks is close to 6.7 meters or, in other means, 0.3 seconds given that the vehicles are driving at speed of 80
km/h. In general, platooning is like a train without rails – indeed the trucks are connected, but not physically. Like the energy-saving ‘drafting’ strategy used in cycling, platooning trucks can utilize more capacity out of the road by shortening the inter-vehicle gap. Normally, the distance is kept between five and ten meters, but it might vary depending on how fast the trucks are moving and braking accordingly. On the one hand, only the driver of the leading vehicle is in full charge of the dynamic driving task and emergency braking is a responsibility of the system. On the other hand, in truck platooning of lower SAE levels the human in the following vehicle is obliged to pay attention and resume control upon a system request. Therefore, the second driver could perform activities that do not divert his attention too much and, above all, the driver might not be allowed to sleep. According to professionals from the European automotive industry (Adaptive, 2015), automated truck platooning strongly benefits from the ‘slipstream driving’ approach which aims at small distances between the trucks to reduce fuel consumption. Figure 8 provides a graphic example of how the wind speed around two platooning trucks changes with the difference in spacing. The wind speed is high at the front of the vehicle but decreases at the back of it and at the front of the following one when both trucks maintain short intervehicle distance. Consequently, decreased air drag contributes to lower fuel consumption. To understand the concept of truck platooning, one could try to imagine the trucks as being communication entities which constantly share information among each other. Wireless communication enables the vehicles to be constantly linked in a virtual way. Backed up by sensor technology, the trucks are able to maintain the same acceleration, braking and steering activity in real-time, thus operating as a single road entity. An example of the technology used during a road test with platooning trucks is provided in Figure 9. As has been mentioned before, platooning relies strongly on ADAS features. Therefore, the market penetration of CACC is very important for enabling the platooning approach in commercial trucking. Although it is expected that in the future passenger cars could also join a platoon, this thesis is limited merely to truck platooning due to its logistics industry focus. The standard technical equipment for automated truck platooning requires a radar and V2V communication modules to be installed on at least two vehicles, allowing them to maintain the same longitudinal and lateral speed in a single highway lane and to communicate data in real-time (ATA, 2015). Given this equipment, the very first developments provide only automation of longitudinal control while the driver of the following truck is responsible for maintaining lateral control.
Figure 9. Example of automation and information systems connected via CAN-bus on experimental vehicle part of the KONVOI project (Tsugawa et al., 2016, p. 72)
The driving automation system detects the truck in front and automatically adjusts the speed, but the driver is still in charge of steering. With respect to the SAE levels of driving automation, this scenario fits into the first category. Such functionality is provided by ACC which, in fact, is standard equipment, offered by truck manufacturers for several years. Nowadays, many trucks are already fitted with the sensors needed for higher levels of automation. Moreover, those included in passenger cars are also believed to be sufficient for automated truck platooning. In addition to V2V and radar, more advanced platooning requires a computer system operating sensors including camera, lidar, GPS, INS, and various ADAS safety features. A simplified picture of those systems and their placing on the truck is shown in Figure 10.
Figure 10. Technology for automated platooning on heavy cargo trucks (Green Car Congress, 2016)
This setup could allow pairs of trucks to maintain both lateral and longitudinal control without the need of human interaction and includes automated operation of braking and accelerating to make both trucks react simultaneously in case of emergency. It is important to note that the combination of different sensors and systems has the potential to overcome limitations of single hardware-entities and their weaknesses (Janssen et al., 2015). For example, bad weather conditions are constraining the use of cameras, but in such a case, a radar can compensate for the overall performance of the system. The same is valid for INS – a system that consists of motion and rotation sensors. Whenever GPS loses signal, for instance while driving in a tunnel, the INS takes over the navigation task and orientates the vehicle from the last point of available GPS signal to the moment when it has regained coverage. With increasing capability of sensor technology and V2V communication, it is expected that automated truck platooning would eventually reach SAE level 3 in the following decade. Next stage developments which feature driver monitoring systems already approach higher levels of automation. SAE level 3 systems would decrease the gap distance, but would still require the drivers to maintain situational awareness, in other words, to observe the environment constantly and to be ready to counteract if the system fails to manage the dynamic driving task. Systems assigned SAE level 4, however, would potentially omit the need of having a human to supervise the driving environment. In the case of SAE level 4, the system would take over both lateral and longitudinal control. Yet, a human would be still present in the cabin to monitor the system and to drive the truck in certain situations, especially outside of highways and during the last mile of the trip. Nevertheless, drivers would be able to engage themselves in tasks other than driving and, for example, conduct administrative work while being attached to the platoon. Similar setup has been tested during the KONVOI and PATH projects, which will be subject to review in chapter 2.5. Regarding SAE level 5 – full automation for road cargo transport is not expected on public roads in the next two decades. More details about the timeline are provided in chapter 2.4. In contrast to some development of passenger cars fitted with automated driving systems, V2V technology is a key factor in automated truck platooning. Linked pairs of trucks rely massively on communication exchange and on-board messages. Still, there is no industry standard for V2V technology and it is expected that first versions of platooning trucks would use OEM-specific software until there are common regulations. Furthermore, digital maps will have the same functionality and purpose as those for passenger cars. Regarding the ADAS features –safety systems such as ESC (Electronic Stability Control), 38
ESP (Electronic Stability Program) and ACC are already offered for commercial vehicles for years. Moreover, the automated emergency braking feature, another significant enabler of automated truck platooning, is expected to soon become mandatory for commercial trucks in Europe (UNECE, 2011). Additionally, one must distinguish between the different SAE levels of automation in the context of automated truck platooning. In order to better understand the difference between operation of single and multiple trucks and the link to the SAE levels of automation, Table 4 presents examples of automated functions for some of the automation levels.
Traffic Jam Assist
SAE Level 2
Description Automated driving on highway up to 40 km/h, hands on the wheel despite automatic steering.
Hands-off, feet-off, eyes-on and monitoring of driver’s attention, lane changes possible in some systems.
Automated Trailer Backing
Automatically drives the composition into a loading dock.
Automated Movement in Queue
At ports or customs where trucks are waiting in queue, start-stop traffic.
Automated Off-Highway Material Hauling
Off-road utilization such as this of mining companies.
Driver Assistive Truck Platooning
Driver conducts steering only. The rest of the dynamic driving task is taken over by the system. Two-truck platoons as a start. V2V communication expected to improve efficiency.
Driver Assistive movement in Queue
Driver only responsible for steering. Braking and throttle are communicated by the front moving truck in the queue.
Highly Automated Platooning
Lateral as well as longitudinal control at highway speed. Minimal gap between trucks.
Automated driving assistance functions in trucks (ATA, 2015)
According to the American Trucking Association, SAE level 1 generations platooning trucks are expected to be utilized in practice provided only with automated braking and throttle, since automation of steering activities does not decrease fuel consumption (ATA, 2015). The same applies for automated moving in queue, where the driver is responsible for steering, but braking and throttle control is communicated to the rest of the trucks via V2V. Moreover, there is a high chance that the first automated platoons would be mono-brand ones, in other words, consisting of trucks made by one and the same
manufacturer. Currently, there is no standard for unifying the systems of different brands. Later, after introducing more advanced SAE levels 2 and 3 features, platooning is expected to evolve to multi-brand formations consisting of more than two trucks (ATA, 2015). Depending on the future system capabilities, shorter gap distance could increase further fuel savings and efficiency. A significant limitation of systems up to SAE level 3, however, is that all the drivers in the platoon must remain situationally aware and must constantly supervise the system. Only after introduction of SAE level 4 functions, the drivers of the following vehicles would be allowed to detach from the driving activity and either engage themselves in tasks other than driving or simply take a rest. SAE level 4 systems would allow transportation companies to save from labor expenses and if legislation allows for that, to extend the hours of daily driving time. From today’s point of view, the future development of automated truck platooning is still unclear and requires legislative changes on an international level. Those and other issues, such as cross-border platooning and resting times will be investigated in chapter 3. The next section will focus on the current vision of the possible timeline and the expectations regarding the introduction of higher automation levels in truck platooning.
2.4 Stakeholders and timeline Before reviewing the expected timeline, it is essential to present the various stakeholder groups that have any relation to automated truck platooning. The stakeholders range from hauliers, freight forwarders and shippers, through law makers and regulation authorities, up to truck OEMs and their suppliers. To speed up the development of truck platooning, all parties should work proactively together towards cooperation, extensive testing and legislative changes. Janssen et al. (2015) divide the stakeholders in four groups – developers, users, policy-makers and regulators. The first two groups have commercial interest in platooning implementation, whereas the other two are those who bear the administrative responsibility. The first stakeholder group includes truck manufacturers who develop and test automated truck platooning. In Europe, among the companies working on this technology are MAN, DAF, Daimler, Volvo, Scania and Iveco (Eckhardt, 2016a). Similar to the developments in the passenger car market, the first trucks equipped with reliable platooningenabling technology will possibly have competitive advantage over the rest of the manufacturers. However, to secure an advantage for the whole European automotive
industry, truck manufacturers should overcome all kinds of barriers by working together. Although automated truck platooning concerns many parties in the logistics sector, the truck OEMs, in contrast to the hauliers, are those businesses who could afford to invest substantial amounts of financial means in the development of new technologies. Nevertheless, if policymakers are to introduce industry standards for truck platooning, manufacturers would rather wait until clear rules are set before investing in something that could eventually happen to not be within the confines of the law. Identically to truck manufacturers, automotive suppliers that develop software solutions and equipment for OEMs would also have an interest in investing in research in development if there were enough market demand. The second group comprises the logistics businesses that are either going to use the technology in daily operations or would indirectly benefit from it. Shippers normally do not have their own trucks but their preference for cheaper and more sustainable hauliers can shift the demand towards transport firms able to operate platoons (Janssen et al., 2015). Hauliers, on the other hand, have a leading role in the development of automated truck platooning. They are the ones who set the requirements towards the truck manufacturers because, ultimately, the transportation business generates the demand for new trucks and operates them. Moreover, in the case of any reliability- or safety-issues, the hauliers would be the first to defray direct losses. In addition, the price of equipment such as sensor technology should make economic sense for the road transportation companies. Equally important, hauliers would be required to make changes in the way they plan operations and schedule transports which could subsequently generate additional overhead costs. At that point, the existence of logistics service providers or a similar service offered by freight forwarders can facilitate the planning process and save cost and time for the hauliers. According to Janssen et al. (2015), back-office and administrative tasks related to planning, information exchange, insurance and profit distribution could be taken over by a newly introduced ‘platooning service providers’ in exchange for a reasonable fee. Next, the second half of the stakeholders mentioned by Janssen et al. (2015) – policy makers and regulation authorities – comprises the parties which do not have direct commercial interest in automated truck platooning but are key enablers and part of its evolution process. Every European country has a ministry responsible for economic matters which could provide funding for technical innovations, if they lie in the interest of the local business. In addition, ministries governing infrastructure, environment and transportation can support innovations with public funds and can pass amendments to allow testing activities on public roads. 41
Figure 11. Vision of development of automated truck platooning in Europe (Eckhardt, 2016, p. 173)
Figure 12. EU Roadmap for Truck Platooning (ACEA, 2017d)
The authorities have direct interest in the increase of competitiveness of the local businesses and should be actively approached for cooperation. They also have the means to support changes in regulations which would undoubtedly be needed. Elaboration on these regulations is provided in chapter 3.4. Moreover, organizations responsible for licensing and inspecting trucks and drivers should issue exemptions adapted to the needs of truck platooning testing. Next, local authorities monitoring the infrastructure and responsible for safety measures should be consulted as well. In the same way, customs and border control have different practices concerning transport trucks which could be affected by platooning. Finally, the insurance companies should change practices regarding liability. A closer look at those issues is provided in chapter 3.4. Regarding the timeline, Janssen et al. (2015) mention 2020 as the year of first commercial implementation. Their forecast foresees an introduction of driver-guarded platoons after 2020 and fully automated ones not before 2030. According to ATA’s forecast, driver-assistive truck platooning, or SAE level 1 platooning, is expected already in 2017 (ATA, 2015). Fully automated platooning with SAE level 2 and higher, however, would be introduced only after 2020. A further relevant source of information that confirms ATA’s forecast is the European Truck Platooning Challenge which took place in several EU countries in 2016 and set the foundation for successful cooperation for the upcoming years. The real-world experiment led to a conclusion that in 2016 SAE level 1 systems were already capable of platooning on European highways. SAE level 3 systems, however, are expected by 2020 and platooning with SAE level 4 of driving automation only after 2025 (Eckhardt, 2016a). This vision is comparable to the previously mentioned sources, despite the difference that introduction of a higher level of automation is expected earlier. The most recent data about the possible timeline was provided by ACEA in 2017. The members of the association are the largest European truck and bus manufactures. They expect mass introduction of partly automated platoons by 2023 (ACEA, 2017c). The exact timing is summarized in the ‘EU Vision Truck Platooning’ (Figure 11) and the ‘EU Roadmap for Truck Platooning’ (Figure 12), together with essential advices concerning regulatory issues that need to be solved and an outline of the administrative support needed by governments to facilitate cross-border operations. Development and testing is planned for the next five years, so that multi-brand platooning with trucks fitted with V2V and V2I communication becomes a reality on Europe’s roads by 2023 (ACEA, 2017c). Looking further into the future, Nowalkowski et al. (2015) foresee deployment of SAE level 5 systems around 2075. Still, Bakermans (2016) makes a valid remark that highways are more suitable for driving automation than urban 44
areas. Consequently, there are chances that full automation of trucks could happen earlier on highways and only later on other roads. However, as of today, it is difficult to make an accurate prediction. In parallel to the incremental introduction of higher SAE levels, over time the truck platoon formation is also expected to evolve. At the beginning, automated platoons would be scheduled by the transport company’s own staff and would consist solely of single-brand trucks belonging to the same fleet. In the future, however, when sufficient market penetration is achieved, co-operation between hauliers should be possible and platoons could be created on-the-go, together with trucks belonging to other road hauliers. According to the ‘EU Roadmap for Truck Platooning’, the goal of SAE level 2 multi-brand platooning and international cross-border transports with two and more trucks is pursued by 2025 (ACEA, 2017c). The roadmap provides a summary of the further action required by the two largest stakeholder groups – truck manufacturers, on the one hand, and regulators on the other. It presents the conditions and requirements set by the European truck manufacturers that need to be met, including those which are beyond the control of the automotive industry. To justify the potential of combining trucks from different fleets and brands, the Dutch TNO (Netherlands Organization for Applied Scientific Research) has conducted a test at the Maasvlakte 2 container terminals in the Port of Rotterdam (Janssen et al., 2015). The researchers managed to measure an average of twelve trucks per minute passing through the nearby tunnel that links the port with the mainland. Given that many of the trucks are loaded for an international transport, the opportunity to build an unscheduled platoon on-the-fly is evident. A similar case holds true for other cargo transport corridors in Europe that are part of the Trans-European Transportation Network. Although the cooperation of European truck manufacturers is concentrated in enabling multi-brand platooning for international transport, several other issues will be presented in chapter 3. Before that, the efforts of multiple industry and academic teams to test automated truck platoons will be investigated in chapter 2.5.
2.5 Projects and road tests The dream of driving automation is not a new phenomenon. Looking back to 1939, one can see General Motors presenting their vision of automated driving at the ‘Futurama exhibit’. The event was sponsored by the company during the World’s Trade Fair in New York, where General Motors showed a concept of circuits embedded in the highway and controlled by radio waves (Bimbraw, 2015). Whilst at that time this was the understanding of automated 45
vehicles, in the following decades the research has been directed towards making the vehicles drive by themselves, independently from the road infrastructure. Later, a major catalyzer for the advancement of driving automation systems has been the DARPA Grand challenge in the United States. The challenge was organized and funded by the US Department of Defense and was aiming at accelerating the research and development of an off-road unmanned autonomous vehicle for military usage. Featuring both cars and trucks, the experiment took place in the Mojave Desert in 2004, 2005 and 2007 (Lipson & Kurman, 2016) and research teams from several US universities took part in it (Chen et al., 2004; Thrun, et al., 2006). Followed by an urban edition in 2007 (Buehler et al., 2009), the DARPA Challenge generated massive know-how in robotic vehicles and machine learning algorithms among the research teams. Consequently, numerous safety applications have been derived from the robotic concepts developed for the challenges and later adapted for civil usage (McBride, et al., 2008). Shifting the focus from robotic vehicles to truck platooning in special, one can trace the pioneering platooning projects back to 1972 in Europe. The ARAMIS project featured twenty-five transit vehicles driving around a test track in France at a distance of 0.3 meters and a speed of 80 km/h (Ioannou, 2013). The vehicles used for the experiment were fitted with ultrasonic and optical range sensors. The project has been repeated once again in 1986. In addition, Ioannou (2013) reports that in 1989 Volkswagen demonstrated a convoy of vehicles on test track, guided by a system capable of maintaining automated longitudinal and lateral control. Nevertheless, the company did not release any technical data due to proprietary information restrictions. Then, another important milestone in the development of automated truck platooning has been the Pan-European PROMETHEUS Project, (short from PROgraMme for a European Traffic of Highest Efficiency and Unprecedented Safety) (Daimler, 2016). Funded by the international Eureka network, the project has been launched in 1986 and has ended in 1995 (Eureka, 2017). It combined efforts of numerous automotive manufacturers, suppliers, universities and governments to conduct research on driving automation systems. The tests aimed at investigating what a safe and efficient mobility concept of the future would be and contributed greatly to the development of functions such as vision enhancement, lane keeping assist, collision avoidance, cooperative cruise control and many others. Nowadays, many of those safety applications are successfully integrated in series vehicles. Next, another European project called CHAUFFEUR has started in 1996 as part of the Telematics Applications Programme of the European Commission and focused on the technical feasibility of automated truck platooning (Schulze, 1997). The tests were 46
conducted on test track with empty trucks and showed fuel savings of 21% for the following truck and 6% for the lead one, operated at speed of 80 km/h and inter-vehicle distance of ten meters. When loaded, the savings at the back of the platoon were expected to drop down to 17% (Bonnet & Fritz, 2000). Moreover, after the opinion of freight forwarders and truck drivers was investigated, it became evident that the business was interested in new technical advancements as a chance to solve some of the existing traffic issues. In addition, truck drivers also expressed positive opinions regarding features that could reduce workload and showed keen interest in human-machine interface systems to support them while driving. The positive feedback led to the establishment of CHAUFFEUR II in 2000. Until 2003, the project successfully tested three-truck platoons and ADAS for heavy cargo trucks, proved the technical as well as operational feasibility of platooning, and established a basis for further research (TRIP, 2017). In the meantime, research on electronically-coupled trucks was undertaken in North America too. Started in 1988 in the United States, the Californian PATH program (Partners for Advanced Transit and Highways) was focused on research on automated driving (PATH, 2017). The project, ran by the University of Berkeley, continues previous research conducted by Shladover (1978) and is funded by the federal government of the United States. Its structure represents a collaborative organization including universities, public agencies and companies from the private sector. As of 2017, PATH is still being funded and research is ongoing. Since the beginning of the 2000s, special research on automated truck platooning is taking place with trucks such as those depicted in Figure 13. The tests aim at Figure 13. Tests with platooning trucks in the desert (Lu & Shladover, 2011, p. 14)
increasing safety and highway capacity, reducing
minimizing traffic congestion caused by trucks. The experiments are based on Class 8 tractor-trailer combinations, a popular truck type in the United States, and investigate platooning with only automated longitudinal control systems of the following vehicle (Tsugawa et al., 2016). One limitation of the research is that platooning trucks are meant to operate on dedicated lines on highways and are thus protected by potentially dangerous behavior of passenger cars. Later on, PATH tested platoons where all vehicles were automated, including the leading truck (Bergenhem et al., 2012). Brief overview of the technology used in the PATH project is depicted in Figure 14. According to Tsugava et al. 47
(2016) after Browand et al. (2004), during the testing of their first and second generation three-truck platoons in 2003, PATH measured fuel savings ranging from 5% to 10% for the leading vehicle and 10% to 15% for the following one at constant speed of 90 km/h. In 2010, new tests were conducted on a closed road in Nevada and on a former military airfield (Shladover, et al., 2006) with distances between trucks ranging from four to ten meters (Lu & Shladover, 2011). The most complete datasets were collected at a steady speed of 85 km/h and at distance of 6m. These datasets revealed a fuel consumption reduction of 4.3% for the lead-truck, 10% for the second, and 13-14% for the third truck (Lu & Shladover, 2011). Despite the promising results, Lu & Shladover (2011) mention that those measurements have been collected at an altitude of 1,800 meters and an air density of only 80% compared to that at sea level. Given that the typical speed of heavy trucks in the United States is 115 km/h on average, the experts expected that the air density at sea level could lead to up to 80% better aerodynamic drag. Accordingly, the latter would theoretically secure up to 50% higher fuel savings (Lu & Shladover, 2011).
Figure 14. Modifications on a truck used in the PATH research project (Tsugawa et al., 2016, p. 74)
One of the very recent projects with automated truck platooning focus in North America was a collaboration between the Auburn University, Peloton Technology, Peterbilt Trucks and several other companies (Bevly, et al., 2015), (Figure 15). The overall goal of the project, which lasted from 2013 to 2016, was the investigation of platooning through onroad tests and commercializing the technology for heavy-duty trucks. Consequently, the
following results were achieved: 4.5% improvement of fuel consumption for the leading vehicle and 10% for the following one. The savings from a two-truck platoon were measured on a test field with a speed of 100 km/h at an inter-vehicle gap of eleven meters (Roeth, 2013). The research of Auburn University also included a business case analysis which concluded that automated truck platooning is almost market-ready (ATA, 2015). As part of
Figure 15. Auburn University – tests with platooning trucks (Roeth, 2013, p. 11)
the same research, additional surveys
tested the drivers’ willingness to operate platoons and showed satisfactory results (Bevly, 2014). Likewise, another international research worth mentioning is the Energy ITS project that started in Japan in 2008 and had a lifetime of five years. All experiments were done on a test track with following trucks capable of both automated longitudinal and lateral control. Focusing on fuel consumption and CO2 reduction, three heavy trucks and one light truck were electronically coupled in an automated platoon. Overall, the research aimed at testing if automated longitudinal control is leading to reduced
consumption, while the rest of the functions contribute to safety and a decrease in the driver’s workload. As can be seen in Figure 16, the tests were conducted with trucks travelling Figure 16. Tests with trucks as part of Energy ITS in Japan (SAE, 2017)
between four and twenty meters. On average, the empty trucks showed 13% less fuel
consumption at ten meters and 18% at 4.7 meters distances (Tsugawa et al., 2016). Similarly, loaded trucks driving at a speed of 80 km/h experienced 8% energy consumption reduction at distance of ten meters and 15% at four meters respectively (Tsugawa, 2014). An additional aspect of ITS was to measure the environmental impact of truck platooning. A simulation study concluded that if 40% of the heavy trucks on a highway would drive in platoon formations, the CO2 reduction would reach 2.1% at a ten meters’ gap and 4.8% at four meters (Tsugawa et al., 2016).
Although the previously mentioned projects contributed greatly to the research on truck automation, they all shared one significant limitation: the data was gathered primarily on test tracks or closed roads. The estimations regarding fuel savings were likely to change during real-road tests with normal traffic. Given these arguments, Tsugawa et al. (2016) after Schmitz (2004) stated that real highway experiments during the KONVOI project (Figure 17) Figure 17. KONVOI project – platooning trucks on a highway in Germany (IKA, 2017)
measured only 2% fuel savings for the first and 11% for the
second truck. Speaking about this, the KONVOI project was started in Germany in 2005 and lasted until 2009. It had a significant impact on automated truck platooning research in real road-world traffic conditions. A team of researchers from the German RWTH Aachen University, MAN and Wabco tested platooning operation with up to five heavy trucks. The goal of the project was to investigate the impact of automated truck platooning on the drivers and the surrounding traffic, as well as to examine legal and economic aspects. In 2009, the research team became a worldwide pioneer in testing a heavy cargo truck platoon in real traffic. Subsequently, the trucks covered more than 3,100 kilometers on public highways (Deutschle et al., 2010; IKA, 2017).
Figure 18. The automated truck platooning system used in the KONVOI project (Tsugawa et al., 2016, p. 72)
Furthermore, the following vehicles were operated under automated longitudinal and lateral control. In addition, the trucks were fitted with V2V and V2I communication systems,
cameras, lidar and radar. The automated platoons featured a lead-truck followed by three automated trucks travelling at a speed of 80 km/h at an inter-vehicle gap of ten meters (Kunze et al., 2009). The latter was selected as optimal due to the increased slipstream effect and the decreased likelihood that random cars can slip in between the trucks (Kunze et al., 2009). During the KONVOI project, the drivers were those who initiated the platooning connection using the help of a driver information system for exchanging data among the vehicles. Simplified representation of the technology used in the KONVOI project is provided in Figure 18. After the success of KONVOI, another platooning project has been started in the European Union. SARTRE (Safe Road Trains for Environment) was a European Commission FP7 co-funded project, conducted in collaboration with seven industry partners including Volvo Trucks (Bergenhem et al., 2010). The project featured both trucks and cars (Figure 19) and aimed at testing multiple automated vehicle platoons at a speed of 90 km/h and an inter-vehicle distance of six meters (GCC, 2017). It ran from 2009 to 2012, whereas the first practical road test was conducted in 2010.The aim of the project was not only to encourage the development of platooning Figure 19. SARTRE platooning trucks and passenger cars (Volvo, 2012)
but, among others, to assess the implications of platooning on public highways with
respect to the human factor, especially taking into consideration the drivers in and around the platoon (Robinson et al., 2010). A significant step forward, compared to previous projects, has been the first-time ever testing on public roads of both automated longitudinal and lateral control of the following vehicles. The tests were not only important to find out how platoons interact with other road members, but helped in outlining strategies for future deployment in mixed traffic. Despite that the focus of SARTRE was not primarily on the fuel savings, the final report mentioned a potential decrease in fuel consumption of up to 20% (Robinson et al., 2010). After the success of SARTRE, the European Union continued to support research on automated trucks. Co-funded by European institutions, the COMPANION project (Figure 20) built upon the findings of its predecessors. With focus on co-operative mobility, the tests were conducted between 2013 and 2016 and combined research teams from Scania and Volkswagen as well as participants from European universities and institutes. The project 51
concentrated the research efforts on the development of a real-time decision-making approach for optimal matching and operation of heavy truck platooning (Eilers, et al., 2015). Moreover, the aim was to create a system that enables multi-brand platooning and cooperation of trucks with different origins and destination routes (COMPANION, 2014). Additionally, it also pointed out legal and standardization issues that need to be solved Figure 20. Scania trucks during testing on public roads (COMPANION, 2014)
before successful market introduction of
automated truck platooning. Overall, the aim of the COMPANION project was to increase the awareness of automated truck platooning in Europe and to showcase its various advantages. Similarly, the Dutch TNO institute is active in conducting research on the challenges related to platooning. Given that the current political and economic climate are very suitable for introduction of platooning on roads, Janssen et al. (2015) mentioned the need to increase mutual coordination in making platooning road-ready. In support of this statement, in 2015 the Dutch government made legislative changes and provided exemptions to allow tests on public roads. The first part of tests was done at the Maasvlakte 2 container terminals of the Port of Rotterdam. The trucks transported containers from a terminal to the customs’ scanner over a distance of 16 kilometers and proved the effectiveness of platooning on short distances. Next, another innovation project being in progress at TNO is focused on the creation of a cloud-based tool to facilitate truck matching in real-time (Janssen, 2017). The tool would include a database of the loads and the travelling direction of trucks seeking to join a platoon and could offer both pre-scheduled platoons as well as on-the-fly solutions for linking trucks without prearrangement (Janssen, 2017). The project involves industry partners in the face of local Dutch hauliers and freight forwarders, as well as the Erasmus University and the Port of Rotterdam (TNO, 2017). One of the most recent events related to truck platooning is the European Truck Platooning Challenge 2016 (Figure 21) which was organized by the Netherlands during its presidency of the European Union. It managed to show that the technology is ready for deployment and identified challenges towards successful implementation on cross-border routes (Eckhardt, 2016a). Organized by the Dutch Ministry of Infrastructure and Environment, the experiment aimed at encouraging development of automated driving in 52
Europe and establishing international cooperation between different stakeholders related to truck platooning (Eckhardt, 2016b). The event successfully involved automated driving trucks by six European manufacturers including DAF Trucks, Daimler Trucks, IVECO, MAN Truck & Bus, Scania and Volvo Group. In April 2016, the trucks departed in platooning formations from multiple European cities to the final destination in the Port of Rotterdam (Wolters, 2016). The networking event was not only meant to showcase the advancement of current technology and ability to safely operate automated platoons on public roads, but also to point out the barriers and opportunities towards more cross-border experiments and subsequent market introduction. The innovative approach of the European Truck Platooning Challenge was the first cross-border platooning
included major European transport corridors such as the Nordic Way and RotterdamFrankfurt-Vienna. Moreover, the opportunity to get together truck manufacturers, logistics Figure 21. Testing trucks are parked during the 2016 European Truck Platooning Challenge (EU Truck Platooning Challenge, 2016)
services providers and governments from different European countries was seen by
researchers and industry professionals as an event with global importance for automated driving (Eckhardt, 2016b). The joined efforts revealed that international integrated action is needed by every stakeholder in order to achieve the goal of having SAE level 4 enabled multi-brand truck platoons on the European roads by 2025. Apart from the projects mentioned so far, other simulations have been co-funded by the European Union recently (Coulon Cantuer, 2016) or done privately by truck manufacturers. For example, Volvo is participating in research projects on automated truck platooning both in North America (Volvo Trucks, 2017) during the PATH project as well as in Europe as part of the SARTRE project and the European Truck Platooning Challenge. Furthermore, DAF participated in the 2015 EcoTwin project – conducted together with TNO institute in the Netherlands, where two-truck platoons with automated lateral and longitudinal control of the following truck were tested (DAF, 2015). Next, Daimler is also engaged in platooning testing with their Mercedes-Benz trucks, equipped with more than 400 modern sensors and sophisticated software (Daimler, 2017a). Practical tests have proven the feasibility of platooning and showed an average of 7% reduction in fuel consumption (Daimler, 2017b). Then, Scania has been actively testing automated truck platooning in 53
Sweden as well (Alam, 2014). Tests showed that substantial fuel savings can be achieved through truck platooning and that the road grade can significantly impact the fuel consumption. Together with other manufacturers, IVECO also partnered in the European Truck Platooning Challenge. Finally, recent headlines state that MAN & DB Schenker are starting a shared project on truck platooning (DB Schenker, 2017). This news reveals the increasing interest of European logistics businesses in investing in automated truck platooning and participating in testing projects to speed-up the market introduction.
Opportunities and barriers
The ‘market and technology overview’ chapter revealed some of the opportunities related to automated truck platooning in the context of European road freight transport. However, next to benefits such as increased safety and capacity utilization, as well as decreased congestion and emissions, several barriers could potentially hinder the introduction of platooning. In the following chapters, various opportunities and barriers will be presented from four different point of views. Accordingly, the observations are structured as follows:
All opportunities and barriers identified during the literature review are depicted in Table 5.
Opportunity Fuel savings
Driver cost reduction
Platooning service providers
Cost of investment
Distribution of earnings
Technology adoption issues
Improved road safety
Improved road capacity
Dispatching and linking issues
Road infrastructure and technical issues
Evolution of the driver's profession
Overreliance on technology
General public reaction
International regulatory action
Regulatory constraints on vehicles
Regulatory constraints on drivers
Insurance, liability, certification
List of opportunities and barriers related to automated truck platooning in Europe
3.1 Economic considerations The economic considerations related to automated truck platooning are divided into six major topics. They will be reviewed in the following order:
Fuel savings Driver cost reduction
Platooning service providers
Cost of investment
Distribution of earnings Technology adoption issues
Although not limited to that, automated truck platooning is partly fact because of the fuel savings and the resulting monetary benefits for the road haulage industry. The motivation of truck manufacturers and hauliers to introduce new technologies in the daily business has its economic justification. According to Zhang (2014), every year wasted fuel and delays account for a total economic loss worth 23 billion dollars. Similarly, some experts state that in Japan alone, trucks are accountable for 8% of the total energy consumption and 6.2% of the CO2 emissions, whereas fuel makes for 18% of the total trucking expenses in 2011 (Tsugawa et al., 2016). In addition, to reason the significance of fuel cost for road freight transport, it is enough to mention that fuel represents as much as 35% of the total cost of truck operators in Europe (Flament, 2016). Janssen et al. (2015) also share the opinion that the fuel savings are among the major motivators behind platooning. As has been mentioned in previous chapters, the savings result from the close inter-vehicle distance and the occurring decrease in wind resistance. Another motivating factor is that cost savings are achieved by everyone in the platoon, although the leading vehicle is usually the one having the least benefit. What is more, every decrease in fuel consumption leads to reduction in carbon dioxide emissions and fine particles. Thus, next to the business value, platooning proves to be a sustainable approach with potential to reduce the pollution caused by road transport operations. As Humphreys et al. (2016) state, given the increasing concerns of the negative impact of greenhouse gas emissions, the fuel savings achieved with the aid of driving automation
systems represent a step forward in decreasing the environmental impact of road vehicles. More importantly, the European Union has committed to decreasing CO2 emissions by 80% to 95% as compared to their level in 1990 (Eckhardt, 2016a). What is more, 60% of these reductions must be achieved by the transport sector (Eckhardt, 2016a). By assuming that platooning technology reaches sufficient market penetration in the near future, a substantial contribution to greener economy is conceivable. Moreover, the business case for European shippers is strong too. As Wolters (2016) reports, on average every five minutes a multinational shipper is dispatching up to seven trucks on the same route to any location of its distribution network. Despite that this is the case for large enterprises only, the fuel saving potential for transportation between distribution centers and ports is evident. Due to the different setup of platooning road tests, it would be ineffective to compare their results in terms of fuel savings. Simulations and practical tests vary because of the differences in technology, truck brand, weather conditions and many other factors. Though the CHAEUFFEUR project measured high fuel savings, the trucks used in the tests were empty. Nevertheless, 17% overall fuel savings were assumed with loaded trailers. Similarly, the PATH project revealed fuel reduction around 10% on average over three vehicles driving in a platoon, while the ITS project measured figures between 8% and 15% on average with loaded trucks. As TNO concludes, on average the overall reduction in fuel consumption is expected to be around 10% per platoon (Janssen et al., 2015). Specifically, they measured that if one truck is travelling approximately 100,000 kilometers per year, and given the fuel prices in Europe in 2015, the potential savings per vehicle would be around 6,000 Euro in total. In a similar manner, participants in the 2016 European Truck Platooning Challenge state that fuel savings are normally ranging between 5% and 15% on average, or around 10% in total (Eckhardt, 2016a). On the contrary, researchers from the COMPANION project considered the business case less optimistically. In fact, the average reduction in fuel consumption was estimated at 5% per vehicle (COMPANION, 2014). Other researchers claim that if 1,500,000 heavy trucks are travelling roughly 150,000 kilometers per year and the penetration rate of platooning is 50%, the total fuel savings in Europe could reach 980,000,000 liters annually (Adolfson, 2016). While the reduction of cost from fuel consumption is one of the major opportunities, it is highly dependent on the length of the gap between the vehicles. As various studies have revealed, the variations between the possible following distances can be significant. To illustrate this, one can look at the values measured during the PATH project (Figure 22). Clearly, the closer to each other the trucks are driving, the higher the fuel savings potential. 57
Indeed, the inter-vehicle gap length depends on factors such as weather conditions and technical properties of the trucks, but on public roads it would be also affected by the surrounding traffic and the law regulations setting the minimum driving distance. The latter case falls into the group of legal issues which will be discussed in chapter 3.4. In addition, measurements of practical tests were mostly done in controlled environment, therefore, realtraffic data might vary greatly.
Figure 22. Measurements of fuel savings of a two-truck platoon gathered during field tests as part of the California PATH project. (Tsugawa et al., 2016, p. 75)
II. Driver cost reduction Next to the expenditures related to fuel, the truck drivers cause 35% of the running cost of the European road haulage industry (Flament, 2016). A similar case is observed outside of Europe too – research data from Japan reveals that in 2011 the personnel accounted for 36% of the total trucking cost (Tsugawa, 2014). Moreover, Tsugawa mentions that the drivers in Japan are ageing and similar trends are expected to happen in Europe and North America too. This assumption is confirmed by reports conducted by the International Transport Forum organization. According to their findings, the trucking industry in Europe and the United States will need around 6.4 million drivers by 2030, while at the same time only 5.6 million people are expected to be willing to work under the current conditions (ITF, 2017). Moreover, this issue is of a two-sided character – on the one hand, drivers are expensive, but they represent a substantial part of the system and cannot be replaced by fully automated trucks in the short term. On the other hand, the trends of an ageing population
would eventually lead to truck driver shortages that can be adressed by introducing automation as in other sectors such as the production industry. While the driver issues with handling driving automation systems will be discussed closely in the following chapters, the purely economic impact of the driver’s role cannot be neglected. The road hauliers in Europe are already facing problems with driver recruitment and this topic is surely among the factors pushing innovations like automated truck platooning forward. Additionally, platooning is perceived as a intermediate step towards fully autonomous or even driverless operation in the far future. On the contrary, systems of lower SAE levels, especially those that do not offer automated lateral control, do not have the potential to change the working environment enough to allow drivers to be simultaneously engaged in something else than driving while they are travelling connected to a platoon. In fact, economic benefits can be harvested through higher asset utilization achieved with truck platooning. The added value in this case represents the time saved from drivers’ breaks. If the driver in a following vehicle is allowed take breaks while being connected to a platoon, the truck can be moved longer on an annual basis. Nevertheless, this scenario would be possible only for higher SAE levels of automation, when the system will be completely responsible for the dynamic driving task and the driver will not be obliged to pay attention or to be attentive at all. Since this development is still years away from deployment on public roads, the asset productivity increase is not seen as a major motivator for early versions of truck platooning. In the future, however, when truck platooning advances to higher SAE levels, the drivers in the following vehicles are expected to utilize the time conducting productive work such as administrative tasks. Moreover, according to a recent study, resting times of 45 minutes a day can be saved if the vehicles regularly switch their places in the platoon (Janssen et al., 2015). To return to the point, the allowed daily driving hours are among the barriers which prevent hauliers from utilizing their fleets more efficiently. Because, normally, one truck is operated by a single driver, the time the vehicle is productively in motion and transports loads is highly dependant on how many hours the trucker can drive a day. Those restrictions are applied in every European country and are perceived by the business as one of the hurdles that can be eliminated with automation of the dynamic driving task. Then again, driving and resting times are subject to various regulations. How the law affects automated truck platooning will be studied in chapter 3.4. Since the next decade will face platooning systems of lower SAE levels, the case of driverless vehicles is not being reviewed in great detail in this thesis. Clearly, two electronically coupled trucks with only one driver can save a significant amount of cost. 59
However, industry representatives share the opinion that in the very short term the same can be achieved by deployment of Road Class 3 LHVs which are able to transport three instead of only two intermodal containers with only one driver (Akerman & Jonsson, 2007). Those vehicles, also known as gigaliners, have a length of 25.25 meters. That being the case, they are 6.5 meters longer than the maximum allowed combination in the European Union. Although they do not comply with the EU directive 96/53/EC (ACEA, 2009), they were allowed by exception in Sweden and Finland due to local environmental and competitive reasons. The Netherlands also tested this concept and decided to change the regulations in order to allow such trucks on the roads (Aarts & Salet, 2012). Furthermore, in 2012 Germany also showed interest and started actions to test LHVs (Heidmann, 2012). Although full automation is clearly the perceived path in the long term, in the short term LHVs have the chance to surpass truck platooning by contributing to truck driver savings without requiring investment in driving automation systems (Heidmann, 2012). Nevertheless, LHVs are not competing with automated truck platooning. Either way, the latter is a logical step in the incremental development of fully automated driving and is expected to advance in future, regardless if gigaliners are deployed on broader scale.
III. Platooning service providers One of the goals of the COMPANION project was to identify how truck matching works in real traffic conditions and to identify strategies in order to build platoons on-the-fly. The project was partly motivated by the lack of previous research related to the questions how automated truck platoons will be created, coordinated, and operated in everyday traffic conditions (COMPANION, 2014). Since an increasing number of solutions to technical issues regarding the sensors and their reliability have recently been found, testing of heavy truck platoons on roads seems more reasonable than it was a couple of years ago. Yet, creating platoons in real traffic is complex and requires additional efforts. Concerning this, the back-office workforce is mentioned as an important factor in the future development of platooning (Bergenhem et al., 2010). For instance, Janssen et al. (2015) conclude that platooning will lead to the establishment of an additional role in the supply chains of tomorrow: that of the platooning service provider. To ensure that random trucks can be linked in a platoon at any time, specialized staff is needed to maintain a database with the schedules and directions of the trucks to quickly help them find a platooning partner. In addition, the truck drivers requesting
a place in a platoon would probably like to be informed about the condition of the leading truck and the resting times of its driver. Accordingly, such information can be provided by the platooning service provider. For this reason, the latter will need to comply with highest data security standards. In addition, those middlemen will be required to act as a trusted party that can ensure fair distribution of benefits among the trucks to avoid favoring one company over another. The platooning service provider can also take over the administrative part of managing platoons including insurance and paperwork. With regards to this, recent news reveals that the Dutch TNO institute is working together with industry partners on an integrated solution to help trucks build platoons in real-time (Janssen, 2017). Such information system would be a first step towards unifying truck matching in real-time. However, the interoperability of trucks of different brands is an essential requirement to be addressed before truck matching systems become a reality. This technical barrier will be subject to further discussion in chapter 3.2. Considering the incremental development of automated driving technology, the driver shortage problem is expected to persist throughout the next years. However, once the technology and legislation develop enough to make automated platooning of higher SAE levels a reality, the displaced drivers can be trained to perform more sophisticated tasks related to dispatching and managing platoons within the haulage companies’ structures. In addition, given the drivers background and by assuming some experience with lower levels of automation, the truckers would be in a favorable position to be employed by a platooning service provider.
IV. Cost of investment A further relevant aspect for the road freight transport businesses is the final price of platooning. Return on investment is a key indicator for every business and freight transport does not make an exception here. With regards to the cost, truck platooning technology will surely require an initial investment by the road hauliers. Therefore, it is essential that driving automation systems prove that operation in real road conditions will lead to a positive business case. Although the prices of technology and sensors are decreasing over the years, nowadays an investment in automated driving equals an amount close to 40,000 Euro per truck (Verlaan, 2017). While during testing period, the cost is covered by the truck OEMs, it is obvious that in the future market-ready technology will be purchased by the road haulage businesses themselves.
According to the literature, the cost of sensor equipment for platooning is expected to drop to levels between 10,000 and 11,000 Euro for a single truck (Janssen et al., 2015). Moreover, Janssen et al. (2015) assume that in the future the cost of investment would even depreciate to 2,000 Euro per truck. To put it another way, for a given seven-year depreciation horizon the annual cost of platooning would be 286 Euro per truck. An additional annual cost of 150 Euro has been estimated for subscription to the services of a platooning service provider, if those activities are not maintained in-house by the haulier. Janssen et al. (2015) state also that advancements in safety features such as automated braking would cause intervals for brake inspections to shorten and thus to further increase the cost-saving potential. Nonetheless, from today’s perspective those numbers are highly speculative and it seems impossible to estimate what the exact cost of platooning technology will be, for example, in a decade. Concerning the cost of investment, the logistics industry might face a dangerous issue if it tries to deploy highly automated trucks with low educated drivers. Indeed, driver trainings require substantial financial funding and those investments should not cancel out the earnings from fuel savings. In a report of the SARTRE project, it is mentioned that the price of educating a driver to handle a driving automation system could reach 1,500 Euro (SARTRE, 2016). In contrast, Janssen et al. (2015) estimated the cost to be around 15,000 Euro per driver. Moreover, with increasing skills the drivers would theoretically become more expensive. Yet, Janssen et al. (2015) argue that the additional skills gained during the platooning training would not lead to an increase of the driver’s hourly wage. Then, systems of different automation levels require different skills from the drivers. Therefore, it is expected that those drivers who have previous experience with systems of lower SAE levels would perform better in the long run, when commercial trucks are fitted with more sophisticated systems. In the end, a profound analysis of the business case is required to ensure that investment in training and technology will not eliminate the potential savings achieved during truck platooning operation. Especially the system cost and the business cases of lower SAE levels need to be investigated in greater detail (Eckhardt, 2016b). What should be added here is that platooning depends on the number of companies using it. Therefore, the first hauliers to invest in platooning will probably bear the highest investment costs, but would eventually have the possibility to profit as pioneers. That being the case, the road haulage industry has higher chances of implementing automation in contrast to passenger cars, but it is also clear that truck operators will only then be keen on investing when the return on 62
investment is high enough (Tsugawa et al., 2016). Therefore, the introduction of platooning in daily operations can be facilitated by examining the business value of platooning together with various stakeholders and authorities. Additionally, joint research is needed in order to investigate how to adapt existing operational practices to platooning without generating excessive costs for the road hauliers.
Distribution of earnings
Next to the investment cost, the distribution of earnings could also represent a complicated issue. Early versions of automated truck platooning will probably rely on matching trucks from the same brand. Those platoons will not face the problem of earnings allocation, since the trucks will likely belong to one and the same company. However, since multi-brand platooning is among the top goals of the future, the distribution of earnings among the platooning participants should be investigated and defined in greater detail. Because the benefits of decreased wind resistance are not equal for all trucks in the platoon, the cost savings should be distributed among the participants according to their contribution. For instance, if a platoon does not consist of trucks from the same fleet and model, the company owning the leading truck should get a larger portion of the profits compared to the operators of the following trucks. This is the case, because the leading truck experiences less fuel savings but at the same time is utilized the most. If this concept is not implemented correctly, the driver of the leading truck would not be interested in being in this position at all. In this case, the introduction of a third party to manage the platooning operations and to distribute the earnings would be the right solution. For example, as has been mentioned before, a platooning service provider or another institution with the required skills could manage the process. Nevertheless, if a third-party organization is managing the earnings distribution, the additional service cost should be reasonable enough in order to avoid pushing the operation of platoons below the break-even point. To conclude, in the case of automated truck platoons with vehicles belonging to different hauliers, there is a clear need for a cost-sharing model to spread the savings equally among all parties. Otherwise, one company might happen to be in an unfavorable position by providing a competitor with cost advantage at its own cost (Janssen et al., 2015). As has been noted previously, a positive sign for solving the issue with earnings distribution is the ongoing research of the TNO institute (Janssen, 2017). However, finding a solution that
satisfies the requirements of all parties is complicated and additional research on possible cost-sharing models is advisable.
VI. Technology adoption issues Like every new technology that is dependent on the total numbers of users, platooning could potentially face a problem of too few participants in its early implementation stage. The lack of market penetration of platooning-enabled systems is a dangerous factor with the potential to slow down the development. An insufficient amount of trucks fitted with systems for platooning could lead to a situation where the investment done by hauliers is not justified. Based on database with real-world cargo trips from the Netherlands, Bakermans (2016) concludes that the unsatisfactory number of trucks with systems for truck platooning automatically decreases the potential number of feasible trips. Consequently, such a scenario puts the economic feasibility of platooning in question. If only the fuel savings are considered a motivation for the hauliers, the market penetration will become a crucial factor for the platooning development. To illustrate this, researchers conclude that truck platooning makes sense on longer distances and with schedules that are made in advance (Bevly, et al., 2015). While surveys reveal that in 75% of the trips the distances covered are long enough and the industry interest in platooning is very high, a potential small amount of trucks able to join a platoon is perceived as the greatest risk in the early years of adoption. In addition, Bevly et al.’s (2015) survey has been conducted in the United States, where the landscape and infrastructure are conducive to longer trips. The highest advantages of platooning are achieved at high speeds and long journeys, and efficiency increases over time and distance. In Europe, however, the routes are considerably shorter and even when the route to be driven is long enough, border crossings are often inevitable. According to ACEA (2017b), 85% of the road cargo is transported during trips not longer than 150 kilometers or less, mainly because other modes of transport are unsuitable for such distances. What is more, less than one percent of the road cargo is transported at a distance over 1,000 kilometers. Yet, there is high potential for platooning to penetrate the market in industrialized areas and in countries intersected by major transport corridors. Moreover, countries with large commercial ports offer drivers higher chances of running across other trucks and thus increase the potential of building a platoon with somebody. As has been mentioned before, platooning benefits multiply with the increasing amount of trucks provided with the required tooling. In order to quicken the adoption of
platooning, sufficient number of stakeholders have to commit to participation. To support this process, local governments and the EU Commission can introduce incentives for hauliers like those offered to owners of electric passenger cars. At the beginning, platoons consisting of trucks of the same fleet and brand would operate without significant troubles. Later, to achieve higher market penetration, cooperation among stakeholders should be established. Likewise, real road testing activities should be encouraged as it has been done during the 2016 European Truck Platooning Challenge. One should not forget that although interested in introduction of new technologies, truck manufacturers are still competitors on the market and are likely to have conflicting business interests. Under those circumstances, continuous dialogue is needed to ensure that the developments by different parties are going into the same direction. In the end, what Lipson and Kurman (2016) see as a risk attributed to the driverless technology of the future, holds also true for automated truck platooning of lower SAE levels: the risk of ultimately failing due to external factors such as the public opinion is not to be neglected. In the period of testing, accidents with platooning trucks might hurt the image towards the general public. Overall, it seems that the press coverage of accidents related to automated driving technology is more sceptical than coverage related to accidents with conventional vehicles. Negative coverage might lead to misinterpretation of the safety capabilities of new developments and turn the people against truck platooning. In fact, most of the autonomous vehicle accidents in the United States happened at low speeds, and in urban areas without the testing vehicles being at fault (Dixit et al., 2016). In the case of automated truck platooning, it is not clear to what extent the technology is safe enough for usage on public roads. Extensive testing is needed to ensure that platooning does not bear any risks to road traffic participants. At the same time, negative public reaction can be avoided with information campaigns conducted in advance. To conclude, although system failures and accidents cannot be precluded at an early stage of development of driving automation systems, and in order to avoid misinterpretations, it is important to analyse the facts in an unbiased manner.
3.2 Technical considerations Truck platooning represents undisputable technical advancement in the field of automated driving. From technical point of view, the incremental development of driving automation systems brings many new opportunities. Nevertheless, though the technology is matured 65
enough for testing to be started, a variety of technical barriers are yet to be addressed. Both aspects will be subject to analysis in the following order:
Improved safety Improved road capacity
Dispatching and linking issues
Road infrastructure and technical issues
Improved road safety
Next to the economic benefits like fuel consumption reduction, automated truck platooning can largely introduce technical improvements contributing to higher road safety. In a world where up to 90% of the road accidents are caused by human errors (Singh, 2015) and 18% of the road fatalities are caused by driver inattention (Robinson et al. (2010) after Broughton & Walter (2007)), automation of critical functions has the chance to drastically improve safety (NHTSA, 2013). The latter is confirmed by the fact that despite the increase in volume of road freight transport over the years, the fatalities caused by accidents with heavy trucks experienced a 45% drop between 2001 and 2014 (NHTSA, 2013). Nevertheless, in 2015 alone, more than 26,000 people were killed and around 1.4 million were injured in traffic accidents on the European roads (ITF, 2017). Consequently, safety features such as electronic stability control, advanced emergency braking and lane departure warning became mandatory for heavy trucks and contributed actively to lowering the number of road accidents and, respectively, the cases of fatalities and injuries in the recent years (ACEA, 2017b). As has been explained in chapter 2, those functions are utilized not only in passenger cars but also for automated truck platooning. Despite the widespread understanding that the truck’s shortened following distance increases the chances of incidents in the case of emergency braking, the road tests have shown that automated driving technology and real-time V2V communication are actually shortening the braking time and thus increasing safety. An important limitation of the platooning concept, however, is the time needed to react if a truck is not fitted with V2V communication, but merely sensor technology is applied. In this case, the reaction of the following truck is triggered only after its sensors have detected a change in the speed of the leading vehicle. When V2V
communication is included in the setup, the following truck can start braking immediately after receiving the emergency message transmitted by the system of the leading truck. This zero-reaction time contributes to higher safety and, consequently, secures social benefits for all road users. In addition, due to the improved reaction time not only the braking ability is enhanced, but also the gap between the vehicles is shortened. The aforementioned aspects of emergency braking are important for the successful operation of platooning trucks, because cut-offs by passenger cars occur often on highways. Equally important, passenger cars might also slip between the trucks and cause them to brake. Accordingly, system developers should ensure that the system is able to cope with dangerous situations when platooning is performed on highways without dedicated truck lanes. In addition, trucks must be visually indicating to other road members that they are connected in a platoon. Although stripes with text would be sufficient for identification, drivers state that visual effects such as flashing lights are a better way of indicating the platoon towards other road users (Eckhardt, 2016b). Next, the ability to brake in case of emergency is only one of the constraints defining the length of the inter-vehicle gap. Most of the studies including practical tests have focused on the optimal distance between the trucks. Tests have been conducted on distances ranging mostly between four and ten meters at speeds of 80 to 100 km/h (Bonnet & Fritz, 2000; Kunze et al., 2009; Lu & Shladover, 2011; Roeth, 2013; Tsugawa, 2014). Nevertheless, many of those experiments have been performed on closed test tracks. What is more, although Janssen et al. (2015) speak of an inter-vehicle distance of only 6.7 meters at a speed of 80 km/h, the American Trucking Association suggests a minimum safety gap of twelve to fifteen meters including the uncertainty margin (ATA, 2015). Clearly, the shorter the gap – the less the wind resistance and the lower the fuel consumption. Nevertheless, the length of the gap depends on several factors such as the characteristics of the truck in terms of its performance capabilities like horsepower, weight, and braking ability. In addition, external factors such as the intensity of the surrounding traffic, the road infrastructure and the weather conditions can also pose an issue. The more negative the overall conditions, the longer the distance between the trucks. Furthermore, in order to ensure highest safety, the driving automation system must be in a place to define the distance by itself and to adapt to changing driving conditions. Regarding cut-offs by other vehicles and especially by passenger cars – the driving automation system will be required to maintain operation even when the physical distance has been increased and one of the communication links has been interrupted. Identically, in the case of such an event, the system should react appropriately and trigger emergency braking on time to avoid collision. However, although cut-ins and cut-offs by 67
passenger cars are common in normal traffic, a study done by USDOT has documented that cut-offs by passenger vehicles are unusual when the inter-vehicle gap is shorter than 30 meters (ATA, 2015). Similar observation has been made during the European Truck Platooning Challenge, when decreasing the following distance from 0.8 to just 0.5 seconds eventually led to less attempts from other truck drivers to merge with the platoon (Eckhardt, 2016b). Nevertheless, drivers who took part in the experiment stated that they have observed drivers of single trucks that try to copy the behavior of the automated platoon by driving closely behind. Accordingly, the latter was considered a very dangerous behavior.
II. Improved road capacity The opportunity to improve road capacity is another positive aspect of automated truck platooning. According to recent studies, traffic congestion is expected to increase by 50% by 2050 (Eckhardt, 2016a). Through utilizing more space on the road, platooning trucks can contribute to decrease the number of traffic jams and to improve the total traffic throughput. Moreover, when equipped with sophisticated software, trucks might be able to plan their routes based on more intelligent road selections. Janssen et al. (2015) measured that given the current length of the heavy cargo trucks, the road usage of single tractor-trailer configuration can be decreased by 46% (in other words, from 82 to only 44 meters), if trucks could be linked electrically in two-vehicle platoons. Anderson et al. (2014) over Fernandes & Nunes (2012) also point out that automated truck platooning could increase the lane capacity measured in vehicles per lane per hour significantly. Because platooning trucks will start with lower SAE levels of automation, the statement of Anderson et al. (2014) holds true only in the case if fully autonomous trucks become part of the traffic. Nonetheless, heavy trucks capable of sensing the environment could respond better to the surrounding traffic and adjust braking and acceleration more precisely even at higher speeds and shorter intervehicle distances. To illustrate this with an example: if 10% of the road vehicles were provided with adaptive cruise control, the traffic congestion would be reduced by 30% (Eckhardt, 2016a). One of the projects for testing of platooning trucks with significant focus on road capacity improvement is PATH. Among others, it is aimed at investigating how driving in a platoon will reduce the required space on highways given the proximity of the trucks. Bergenhem et al. (2010) after Michael et al. (1998) state that the increase of lane capacity is expected to reach factor two to three if passenger cars are connected in platoons together 68
with trucks. While this assumption concerns a case when all road vehicles are able to platoon, it still supports the argument that platooning would eventually lead to improvements in road capacity utilization.
III. Dispatching and linking issues Though automated truck platooning can bring forth benefits, it can also add an additional complexity to the working routine of the dispatchers (ATA, 2015). In the early stage of implementation, it will be unlikely to see the last mile of the route automated. Congestion in yards and docks and especially on shipping lanes is expected to increase if platoons of multiple heavy trucks begin to arrive simultaneously. Nowadays, transportations businesses operate complex systems organized to match loads, drivers and schedules. Adding the platooning concept to the mix is likely to further increase the complexity and to create the need for additional optimization models, which will need to be additionally tested and implemented to handle the new scenarios. Equally, this might directly affect the planning and dispatching software tools which would require an update to include platooning trucks in the calculation. On the other hand, matching trucks that travel in the same direction might be exacerbated by traffic and congestion issues. Furthermore, arrangements between transportation companies cannot always be met in advance. Accordingly, waste generated due to formation time issues should be compensated with other benefits, otherwise the waiting time can eliminate the profits achieved during the platoon operation (Bakermans, 2016). An additional issue arises from the need for analysis of the likelihood of finding a platooning partner. According to Auburn University’s analysis of a circa 480 kilometers route in the United States, the likelihood of building a platoon with the available dataset lied between 3% and 45% (Bevly, et al., 2015). Moreover, truck platoons did not cover the entire route together, but only a portion of it which varied between 30% and 75% (Bevly, et al., 2015). The high variability of the findings is a troubling fact. Those results point out the need for further investigation of traffic and routing data that could be provided by road haulage companies and analysed during academic projects. An analysis of comprehensive datasets with records of international road transports could reveal the possible likelihood of building platoons on major transport corridors within the European Union. For example, best-practice cases of truck platooning are available in the BESTFACT consortiums database. Part of this database is the GOFER system which is an example for a cooperative freight management
and regulation approach which can be used as a basis for future research (BESTFACT, 2015). Likewise, the current Truck Matching Project by the TNO institute is a step in the right direction to find a solution for building platoons on-the-fly (Janssen, 2017). Looking forward to the future, increased automation could enable both passenger cars and trucks to join platoons. This concept has been subject to research during the SARTRE project (SARTRE, 2016). It is also expected that in future a truck or a passenger car might be able to reserve a place in a platoon that is travelling in the same direction (Adaptive, 2015). Nevertheless, from a technical point of view, coupling vehicles from different brands could pose an essential issue. So far, information and communication technology systems from different manufacturers have not been tested as part of the setup in one and the same platoon. Those technical limitations will likely undergo a change in the next few years, since the European Commission is currently funding research projects for standardization of driving automation systems. Finally, automated truck platooning is expected to change the supply chain organization of logistics service providers by shifting the focus from volume to value. The requirements for successful cooperation between different parties in the supply chain will increase and the processes will need to be refined to gain advantage of platooning. One idea is to organize the transport companies in clusters, according to their shared characteristics in terms of average distance, frequently travelled direction, and nature of transported goods (Eckhardt, 2016a). Still, linking on-the-fly and meeting at a designated location are two scenarios with completely different levels of complexity. Whilst clustering might help to facilitate the process, it will not solve the issue completely. Meeting at a pre-scheduled place and time might be easier from an organizational point of view, but could lead to efficiency decrease as unexpected events can interrupt the schedule anytime. To conclude, implementation of multi-brand platoons and operation of trucks by different manufacturers and with differing characteristics is an important requirement for the successful business case of platooning (Eckhardt, 2016a).
IV. Multi-brand platoons As has been mentioned before, the lacking interoperability of platoons that affects trucks of different manufactures is possibly one of the most significant barriers towards successful platooning introduction. To ensure that heavy trucks from different fleets can platoon together, their driving automation systems must be able to interpret the performance
parameters and road data in one and the same way. Currently, every truck manufacturer is developing their own solution and the scope of every research project features only platoons consisting of trucks of the same brand. Under those circumstances, to experience economies of scale in the future, truck manufacturers have to work closely together to enable the truck connectivity even when the vehicles are of different brands. Regarding cooperation, Bevly et al. (2015) point out that between 68% and 71% of the cargo transports are done on the same routes, thus revealing a great potential for mutual coordination. However, their survey documented that companies were more likely to build platoons solely within their own organization to avoid helping a competitor to perform better. Certainly, it is hardly possible that the needed unification will happen quickly. According to Janssen et al. (2015), in the beginning trucks from the same fleet will platoon in pairs due to the relative simplicity from operational point of view. Later, after technology has advanced enough and in order to maximize the economic benefits for the road haulage industry, driving automation systems should be made interoperable to allow vehicles from different manufacturers to cooperate. Furthermore, industry standards regarding technical requirements for platooning need to be set in terms of performance (power, braking, acceleration, weight) and equipment (software, radar, lidar, V2V etc.). Currently, the European Union is funding initiatives to ensure that in future all safety features and sensors in heavy trucks are engineered according to common industry standards in order to allow multi-brand platoons to operate safely (Coulon Cantuer, 2016). Different characteristics regarding braking, weight and performance specifications are going to be matched and maintained in a shared database to allow road haulage firms to safely manage the order of trucks when the platoon is multi-branded. Another possible strategy for achieving interoperability among different truck OEMs is to rely on the existing safety standards originating from the automotive industry. While technical feasibility of sensor technology for platooning trucks is constantly evolving, achievement of sufficient safety for public road deployment is not yet confirmed. For example, Nunen et al. (2016) designed and proposed safety-related simulation based on the ISO26262 automotive safety standard to ensure that automated braking would not cause a collision. Similarly, the derived scenarios have been tested and implemented on test trucks. Such experiments are generally useful for setting the optimal system design and requirements in order to increase the safety level of future driving automation systems. A further possibility is to adopt the automotive AUTOSAR (AUTomotive Open System Architecture) software standard that prescribes how to design software architecture components for electronic control units. Together with existing CAN (Controller Area 71
Network) standards, ISO26262 and AUTOSAR can serve as a worthy basis for establishment of platooning-specific software and hardware development standards. One step towards achieving this goal has been done by SAE (2014) with their J2735 standard for short range communications, as well as with several NHTSA’s safety standards concerning V2V communication with respect to heavy vehicles. According to automotive industry professionals, the technology needed for platooning with trucks of the same manufacturer is already there, but since the hauliers will be more interested in multi-brand operations, efforts should be directed towards ensuring interoperability (ACEA, 2017c). In order to respond to the industry needs in Europe, 350-million-Euro funding was devoted to research projects on connected and automated driving by the European Commission (Coulon Cantuer, 2016). Among them, 15 to 20 million Euro were reserved for projects for multi-brand platooning in real traffic conditions – which is announced to start in 2017 (Coulon Cantuer, 2016). Nevertheless, technical aspects such as functional safety, cyber security, and reliability of sensors are among the barriers with potential to decelerate the pace of developments. Therefore, those need to be addressed seriously in the upcoming years. To conclude, in contrast to passenger cars and smaller commercial vehicles, heavy cargo trucks share more homogenous performance specifications (Ploeg et al., 2011). Thus, the opportunity to adjust the existing regulations according to the needs of automated truck platooning is greater than in the case of other vehicles. Anyhow, the possibility to operate multi-branded platoons has the potential to increase the financial opportunities for the hauliers. That being the case, it is positive that regulators have recognized the need for further research on interoperability and common development standards.
Road infrastructure and technical issues
Though it looks like development and testing of platooning technology does not demand any changes in the existing road infrastructure, this is not completely true. According to industry experts, first tests already revealed use cases where platooning meets barriers. One such scenario involves the limitation of how many tons a bridge is engineered to hold. Given the short gap between the trucks, the maximum allowed tonnage of a bridge could pose a safety risk and would require the platoon to be detached before passing. Although Janssen et al. (2015) do not expect the bridge strength to represent a major barrier, possible issues should be analysed in detail. The same applies for roundabouts and on/off ramps, and especially road-side barriers which are designed to absorb the impact of a single vehicle only
(SARTRE, 2016). Eventually, national road authorities might need to do changes concerning the road infrastructure before it can be used by trucks platoons. Next, tunnels, slopes and road curves might pose a risk and require the trucks to detach. Though serious failures have not been observed so far, this might be due to the limited number of experiments with platooning trucks conducted on public infrastructure. In this regard, more is to be done to investigate the potential risks of truck platooning for the existing road network and to test platoons in complicated traffic situations involving traffic jams or roadworks (Eckhardt, 2016b). Then, in addition to the road infrastructure issues, the security and durability of today’s technology can bear risks for platooning trucks too. Similar to other electronic systems, the technology used for platooning can also encounter problems with security. Fitted with various communication modules and an on-board operation system, hacking of vehicles through their Wi-Fi networks or other sensor interfaces is considered an issue worth further exploring. Above all, cybersecurity is a general problem for modern road vehicles in general and is not limited to connected trucks. But even so, by default the V2V communication systems present a significant security measure because these have been developed for security per definition. Then, not only the V2V communication infrastructure should be fitted to the needs of truck platooning, but the HD maps used in modern navigation systems will also need a special attention. To guarantee functional safety, automated truck platoons need to get reliable, real-time road traffic notifications. Considering the advancements of smart mobility technology and the presence of strong telecommunications industry in Europe, providing reliable real-time data transfer should not be a major barrier. Along with dispatching and linking issues, there are other technical problems which are valid for autonomous driving cars in general. ADAS undoubtedly improve safety, but the average human driver is still considered statistically safer than then an electronic system. As Tsugawa et al. (2016) point out, the mean time between failures of a human driver is very long, which could be interpreted as a sign of high reliability. They mention that in Japan, and comparably in other industrialized countries, statistically a single driver is subject to an average period of 500 years between fatalities and three years between injuries in normal driving conditions (Tsugawa et al., 2016). However, when the driver is asleep or distracted, the figure falls down to minutes. The conclusion is that the largest opportunity for ADAS is the potential to improve safety in co-operation with the human driver. As Lipson and Kurman (2016) cite Tesla’s CEO Elon Musk, the true complexity of automation is not to engineer a system able to achieve 99% correctness, but one providing 99.9999%. For this 73
reason, if a system can entirely replace the human in an early stage of implementation and without the proof of being entirely safe, the chance to encounter problems later is evident. Accordingly, testing of automated truck platoons with lower SAE levels is a suitable opportunity to figure out how to improve the human-system interaction. Tsugawa et al. (2016) suggest that before deploying platooning trucks on public roads, the system behavior must be tested by fitting vehicles with sensors and software and letting the human drive, while the system is switched on, but does not control the vehicle. In the end, the human decisions can be compared with what the system would have done during the drive to disclose potential errors in the software algorithms. Since the human-machine interaction has several other aspects in the context of automated truck platooning, this will be subject to study in the next chapter. Finally, although the current state of platooning technology is considered safe, findings from industrial workshops suggest that first platoons for public roads should be deployed in a controlled environment. For example, dedicated truck lanes such as those that are being used in peak hours in some countries can serve to ensure safety of early platooning versions (Eckhardt, 2016a). This will be required at the start, because trucks will still have different characteristics when related to loading, braking and engine specifications. Because proving a sufficient level of safety is essential, the dedicated lanes could be a first step towards examining how platoons are performing in regular mixed traffic.
3.3 Human considerations On the one hand, implementation of automated truck platooning with focus on economic and technical aspects is a concern to the industry and governments. On the other hand, the human driver is the one who will spend most of the time dealing with this innovation in routine working conditions. Therefore, in this chapter the human considerations will be studied as follows:
Evolution of the driver’s profession Workload reduction
Driver awareness and distraction
Evolution of the driver’s profession
Automation: a threat or on opportunity for the drivers? At first thought, mentioning automation provokes imaginative scenes of machines taking over the human jobs. Job automation could possibly be interpreted wrong among workers of the road haulage industry but, in fact, the opposite scenario holds true. A recent EU study on the effectiveness of professional driver trainings concluded that the road haulage sector is going to experience a shortage of manpower in the upcoming years (Panteia, 2014). The negative trend can be seen clearly in Figure 23. Moreover, the International Road Transport Union suggests that in the next two decades alone, more than one third of the German truck drivers will retire (IRU, 2017). Consequently, this is going to open a gap in the labor market in Germany, meaning a shortage of approximately 150,000 truck drivers.
Figure 23. Baseline projection of heavy truck drivers supply. The data for Europe consists of EU 27, Norway and Iceland (ITF, 2017, p. 32)
Additionally, the average truck driver age is also increasing – since 2012 it went up from 46 to 47 (IRU, 2017). The shortfall is caused by multiple factors including the lack of skills and the simultaneously increasing regulatory requirements towards driver workforce, the deteriorating image of the profession, and the relatively uncompetitive remuneration that, however, still accounts for 30% to 40% of the haulier’s total operating expenses (IRU, 2017). In addition, many of the drivers who are working today are at later career stages and are expected to retire in the upcoming years. At the same time, less younger people are attracted by the trucking job than before. To put it another way, the increasing volume of the global 75
trade meets an industry where one third of the drivers are already over 50 years old and are expected to start retiring in the next decade. The same problem has been identified by several organizations including the Swedish Road Operators Association (NLA, 2017) and confirmed by the European Commission (Lodovici, et al., 2009; EC, 2012). The trend is not unique to Europe though. The American Trucking Association reports similar findings and is concerned about the driver shortage issues that have the potential to block further economic growth in the transportation industry (Costello & Suarez, 2015). Figure 24 shows that the projection of the demand for commercial road freight transport is increasing, contrary to the trend of declining workforce. Under those circumstances, the driving automation systems will likely not displace the drivers from their current jobs. In contrast, if reactive measures fail to address the problem on time, the expected driver shortage problem can potentially disrupt the road freight sector. Therefore, automated truck platooning is rather an opportunity to create new jobs and keep the economic growth in the transport industry.
Figure 24. Heavy commercial vehicle road freight demand (billion vehicle kilometers travelled). The data for Europe consists of EU 27, Norway and Iceland (ITF, 2017, p. 35)
Given the driver shortage issue, the driver satisfaction is crucial for the success of truck platooning. In the first place, truck drivers should take part in the technology adoption process. To make this happen successfully, they should be familiarized with the benefits that technology offers to them. According to recent surveys, transport managers share the opinion that driving automation systems would have a positive impact on driver retention and that drivers would willingly use the system (ATA, 2015). On the contrary, owner-operators 76
themselves shared a rather negative opinion and tended to reject the usage of such systems (ATA, 2015). The conclusion on this contradiction is that the latter is caused by the relatively low profit margins in the cargo industry in the United States and the limited investment opportunities. However, while one survey is not representative for the entire road freight transport sector, it is evident that industry unions and governmental organizations should support the innovation process to counteract the arising truck driver shortage problem. The fact that a similar shortfall exists even in countries like Switzerland, where the truck driver remuneration is higher than in other European countries, points out to the conclusion that just paying the drivers more is not going to solve the problem (IRU, 2017). Along with solutions like improving the working conditions and offering social benefits, investing in innovation such as automated truck platooning is a key factor for helping the industry avoid negative scenarios. Still, affirmative feedback has been gathered during the European Truck Platooning Challenge, when truck drivers expressed positive opinion on the supporting applications and the chance of increasing road safety (Eckhardt, 2016a). The drivers showed keen interest in the new technology and its innovation potential. What is more, the opportunity to acquire additional skills and to decrease workload was among the aspects mostly liked by them (Eckhardt, 2016a). In contrast, drivers expressed concerns about the behavior of the system in unpredictable emergency situations as well as about the reaction of other road users. Although fully autonomous trucks have the potential to change massively the driver’s profession in the long run, this scenario is still far off the radar as far as public roads are concerned. Nowadays, the driver is a substantial part of the system and can be hardly replaced in situations such as driving in the city or attaching a trailer. When automated truck platooning is introduced on a large scale, lower levels of driving automation in form of different ADAS functions will be available to the truck drivers. In the following years, it is expected that the travelling attached to another truck would enable the driver to conduct tasks other than driving to utilize more working time. This job change might include administrative work and some state that later has the potential to shift the driver’s duties towards transport manager of the truck (Janssen et al., 2015). In the end, given the expected driver shortage, additional tasks and responsibilities have the potential to increase the attractiveness of the driver’s profession. Nevertheless, even driving a following vehicle as a part of an automated platoon brings challenges different than those of single truck operation. Drivers who participated in the European Truck Platooning Challenge stated that the driver of the leading truck tends to 77
think in a different way concerning the management of the pace of the platoon as a single vehicle entity (Eckhardt, 2016b). In addition, the driver watches out for potential problems and monitors the road traffic more than before (Eckhardt, 2016b). Accordingly, there is a need of establishing proper methods to effectively train the drivers and making them aware of the capabilities and limitations of driving automation systems. Nowadays, common practices include the usage of simulators or real-world trainings on test tracks. The tests performed on closed infrastructure can prove to be very effective in analysing driver issues, but can also be used for training purposes in parallel. For example, issues arising from the close distance between trucks and general issues with the system interface can be investigated on test fields or terminal yards. Yet, educating drivers comes along with financial investments. A profound business case analysis is needed to ensure that investment in increased qualification of the drivers pays not only for them but for their employers too. As has been mentioned before, investment in trainings should not cancel out the overall benefits from truck platooning, otherwise the industry interest of implementing driving automation systems could vanish. Additional tasks and trainings could lead to job enrichment and make the image of the truck driver profession more attractive than ever before. Despite that such a drastic change is likely to have a positive impact on the industry, the increased qualification can also lead to potential loss of skill. The latter can occur when the drivers start to engage the driving automation systems too often or simply lose practice over time. Similar example from the aviation industry are the pilots who switch off the autopilot from time to time in order to escape monotony and skill deterioration. To conclude, it could turn out that the additional features have the opposite effect on the skillset of the truck drivers. Therefore, specific research on the potential loss of skill is needed to investigate in detail if this aspect really poses an issue.
II. Workload reduction The driver’s skillset is not the only thing that can possibly change after a large-scale introduction of automated truck platooning. Likewise, the driver workload is another aspect that is going to evolve. Even though researchers expect an overall driver workload reduction, it is not excluded that driving in a platoon can lead to negative consequences as well. Overall, driving automation systems represent an opportunity to unburden the driver from multiple stressful activities. Previous research has identified that after introducing
automation, the driver’s duty shifts to a monitoring tasks and significantly facilitates the driver’s job (Stanton & Young, 2005). On the contrary, others state that when implemented in a wrong way and without respect to the driver issues, automated systems can increase the perceived workload (Norman, 2009). At times when simultaneous complex tasks must be executed or the system fails to deliver to desired behavior, automation can have a negative impact on the driver. Furthermore, if the driver starts to feel overwhelmed with simultaneous tasks, the stress level can increase and the job can be perceived as too demanding. Consequently, driver’s efficiency and productivity should be evaluated closely during platoon operation to conclude how the workload really changes. The responsibilities of the driver also have a significant impact on the perceived workload. In the case of fully automated lateral and longitudinal control, the driver in the following vehicle would have the possibility to engage tasks other than driving. However, no consensus is achieved so far regarding the activities that can be conducted in the meantime. On the one hand, the driver is released from the duty of conducting the dynamic driving task. On the other hand, with only low SAE levels available in first versions of platooning it is expected that the driver must take over control anytime when the system requests such an action or the driver is not confident about the system’s performance. This contradiction poses a limitation on the extent to which a driver can fully engage in doing something else other than driving while the truck is linked to a platoon. Additional research is needed to investigate what is the possible and recommended degree of engagement in tasks other than driving with respect to systems of different SAE levels. Next, because automated truck platooning has not been tested enough outside controlled environments, there is limited research that documents the impact of platooning in relation to the driver experience in real road traffic. In the first place, investigations need to be done in order to analyze how the driver reacts after longer period of driving closely behind another vehicle. In addition, the short following distance comes along with minimal field of view. Seeing only the trailer and a small portion of the road has the potential to deteriorate the alertness of the driver. In addition, road spray and bad weather conditions could increase driver’s fatigue. The impact of those factors can increase if the driver is not allowed to take a break while automated driving is switched on. Furthermore, despite that breaks during travelling are dependent on the degree of automation the system is capable of, they also fall into the group of regulatory issues which will be subject to review in chapter 3.4. Concerning the limited view, De Bruijn and Terken (2014) propose a concept for facilitating cooperative driving and enhancing situational awareness of drivers by using a 79
camera that displays images from the leading truck. Thus, drivers will be able to see what happens in front of the platoon on a special display in their vehicles (Eckhardt, 2016a). Finally, simulator studies are revealing that platooning might provoke negative effects on driver deterioration, but additional research is needed to draw a substantial conclusion. To conclude, behavioral adaptation is a significant challenge linked to the introduction of driving automation systems due to the possibility of unexpected changes in the reactions of the drivers (ATA, 2015). Once factors such as speed, distance and braking change, the driver might also show a change in behavior. To sum up, the acceptance of platooning is dependent on how well the drivers will adapt to the decreased spacing between the trucks and the changes in the workload. Since inter-truck gaps are expected to drop below 0.5 seconds, truck drivers might feel unsafe or unwilling to drive at such a proximity (Janssen et al., 2015). However, participants in the European Truck Platooning Challenge stated that the short distance does not pose a problem, but rather the steepness of the road causes stress. When a platoon is approaching on-ramps and off-ramps, the drivers normally feel uncomfortable. Nevertheless, drivers report that, at the end, driving automation systems function very adequately and according to their personal expectations (Eckhardt, 2016b).
III. Human-machine interface Another aspect of automated truck platooning is related to how the driver interacts with the system. The human-machine interface is playing an essential role in assuring successful platooning operation. In order to satisfy the drivers’ needs in terms of comfort and to facilitate their work, the interface must be as user-friendly as possible. In any case, the driver should be able to monitor the performance of the truck anytime and to be in control of all key functions, such as joining to a leading truck, handling requests to join from following vehicles, dissolving from a platoon, and many more. Moreover, the platoon dissolving process must happen in a way that allows the driver to gradually take control over steering, acceleration, and braking. These actions should be clearly indicated in the cabin through a user interface similar to passenger car’s infotainment systems. The display should serve as a primary tool for monitoring the dynamic driving task, the behavior of the system and the condition of the truck. Moreover, it should be able to show additional information like the performance parameters of the other trucks in the platoon. As De Bruijn & Terken (2014) mention, a second display showing real-time images from a camera in the leading truck can
greatly contribute to helping the driver in a following vehicle to stay aware of the traffic situation in front of the platoon, all this despite the lack of direct eye contact. Because driving automation systems in heavy trucks rely on sophisticated software and hardware, it is vital that the user interface of the system can summarize and present the data in a simple and understandable way. In order that safe operation is ensured, the truck drivers need to understand the system and its behavior by only looking at the performance indicators on the screen. This knowledge should be acquired during trainings where the drivers can be taught how to use and control the system properly, as well as to understand its limitations. For example, emergency braking triggered at a certain distance from the leading truck should not surprise the driver. On the contrary, any action of the system should be rather expected by the driver. In addition, at any given moment the driver should know which automation mode is engaged. In the same way, the system should show clearly which features are switched on and should display important performance parameters so that the driver is always aware of what is going on with the vehicle, without investing too much time to look at the screen. Yet, because truck platooning is a new concept, the amount of research towards developing trainings for usage of platooning technology is very narrow. Since platooning relies heavily on the skillset of the human driver to supervise the system and to take control in emergency situations, industry and regulators should draw attention to the training needs and should develop training programs to ensure safety operations. Moreover, additional research should elaborate on the optimal user interface so that automated truck platooning is operated in synchrony with the driver’s requirements. Finally, simulator testing is normally used to assess the reactions of truck drivers in different situations. However, real road testing is still limited, therefore findings from simulator studies are considered some of the most important data sources in the current stage of development. To illustrate this, during a recent industry workshop the research team observed the lack of reaction of the drivers in following trucks, as the leading truck started to change lanes or even to drive towards the emergency lane (Eckhardt, 2016a). The conclusion based on this finding is that the driver of the leading truck should not be responsible for the actions of the entire platoon. Thus, the human-machine interface can be used to engage the drivers of the following vehicles to a certain extent in order to hinder them from relying completely on the leading vehicle. Alternatively, the system can warn the drivers of the following trucks if the leading vehicle starts to perform dangerously.
IV. Driver awareness and distraction As has been mentioned before, assuming the increased usage of automated functions in the vehicles, over time the driver’s skill might deteriorate. Correspondingly, the danger caused by overreliance on technology is typical issue related to early forms of vehicle automation (ATA, 2015). Hauliers who use driving automation systems in their trucks must secure that the drivers are aware of the system’s weaknesses and its limitations. At the end, SAE level 1 and 2 still require that the driver is conscious and upon a system request can resume operation of the dynamic driving task at any given moment. Otherwise the human driver might become too dependent on a system that is not capable of providing the expected reaction in 100% of the time. This potential issue can be eliminated if the drivers are educated that they are still the primary control system of the truck. This should hold true regardless of how many automated functions the truck is fitted with. Furthermore, it is important to make clear in which situations the driver must intervene. Next, more tests and trainings are needed to explore how different trucks respond to different emergency situations, since it might happen that one and the same truck model is equipped with different sensor technology, not to mention trucks manufactured by different OEMs. Then, another issue related to automated driving is resuming control in higher levels of automation – those from SAE level 3 and higher. If a platooning truck is relying on automated driving for a very long time, longer than a few hours – will the driver be able to resume control if, for instance, weather conditions change rapidly and the system requests a human action? Such scenarios have been investigated by Rauch et al. (2009), who pointed out the importance of keeping the driver aware after a long period of following another vehicle. At the point of time when the control is transferred from the system to the driver, a possible critical situation can provoke an accident caused by the lower level of driver alertness. The process of transition from system to human control is one of the key factors of successful truck platooning and must be investigated thoroughly to ensure highest safety for all road traffic participants. Without a doubt, special trainings will be needed to prepare the drivers for any situation before they can start operation of platooning trucks on public roads. In fact, due to the very short gap between the trucks, the driver is expected to react slower in emergency situations – a potential problem that must be considered by the system engineers. Such latency in the human action has been reported by Dixit et al. (2016), who found out that the reaction time increases with the number of kilometers covered when automated driving mode is switched on. Moreover, due to the possible decrease in workload
related to higher SAE levels, it is believed that the driver can engage in secondary activities. Hereof it is important to define a standard way of monitoring the state of the driver at any given moment to secure that the platoon is operated as safely as possible. Furthermore, experts alert that in the long run automation of the driving task can potentially lead to lack of driver awareness (ATA, 2015). What is more, driver awareness is as important for early versions of truck platooning as it is for driving any other vehicle without automated driving functions. At the beginning, human drivers will not be permitted to sleep while being linked to another truck. In order to ensure that the driver is maintaining a sufficient level of attention, one possibility is to adopt ideas from passenger cars. A small in-cabin camera can be used to monitor the driver’s eye in real-time and to issue an alert, if the driver falls asleep or loses attention by looking aside for a longer period of time. Although automation can replace many of the driver’s functions in the cabin, the driver remains the primary element in the system. Accordingly, latest research shows that drowsiness can increase because of the monotony of following another truck for a longer period of time. Such a scenario is described by May & Baldwin (2009) as a state of unintentional inattention. In addition, what is understood today as a distraction might change its definition with the introduction of first platooning trucks in the next years. For example, taking eyes off the road and conducting other tasks with a tablet or a notebook might be admissible, however, it should be examined to which extent the human is able to switch back to the dynamic driving task after spending longer period of doing something else. Recent research
electrocardiography measurement revealed that driving automation systems help the drivers to reduce stress and workload (Daimler, 2015). Apart from the driver’s statements that the systems made their working experience easier, the study has shown that the fatigue levels were lower and the drivers were generally more attentive. Consequently, conducting other tasks while driving in a platoon brings more diversity to the workday and contributes to decreasing the drowsiness by 25% (Daimler, 2015). Moreover, drivers stated that they felt themselves more vigilant while testing driving automation systems. However, some trade unions are sceptical about taking breaks and remaining alert at the same time, which is an issue that needs further investigation through practical tests (Eckhardt, 2016a). Next, situational awareness in the context of system operation, as defined by Endsley (2012), has a significant impact on making decisions regarding the dynamic driving task. As such, the latter implies higher level of complexity. If the driver experiences a lack of situational awareness, the chances of human error or misjudgement increase greatly. In this 83
regard, Endsley (2012) defines three separate levels of situational awareness that are mentally executed during driving. According to the first level, the driver is sensing the environment through the five human senses. The driver is confronted with different roadway characteristics and vehicle information communicated through displays and alerts. An error can happen when the driver is unable to determine the required information. In this regard, the cause can be based on two options – a wrong signal or driver’s fault. In the first case, the information is presented in a confusing way or is veiled by another extraneous information. In the second case, the distracted and unaware driver is failing to interpret the environmental characteristics and cannot judge the situation properly. Further, the second situational awareness level depicts a scenario where the driver interprets all elements and assesses the situation. In the third level, based on this assessment, the driver draws conclusions about the immediate moments of time and decides for a suitable action. Thus, the action taken by the driver relies on the information that has been processed on levels one and two. Possible errors can easily happen if the driver fails to assess the environmental factors in first place. Lack of understanding about how the system works and what its signals mean or, alternatively, loss of attention and lack of enough information about what is happening around the vehicle might also be among the possible causes. This example is of special interest regarding automated truck platooning, because the drivers of the following vehicles might get a chance to distract themselves and mentally detach from the driving process. Consequently, lack of situational awareness could lead to misjudgements and failures – a situation with potential dangerous implications, especially on public roads such as highways and those with lots of other traffic participants. To conclude, the topic needs additional investigation and should be taken into account by system developers in order to provide the driver with sufficient time buffer to take control over the vehicle even when lack of situational awareness occurs.
General public reaction
Finally, a potential barrier for automated truck platooning can be the social reaction. Indeed, the truck drivers play a central role in the concept but they are not the only road users affected by platooning. Although truck platooning is offering substantial benefits for all road users, the society needs to be more informed about the opportunities and barriers of this technology, otherwise a negative reaction is possible.
First, truck drivers should be educated on driving automation systems in order to understand that the technology is there to help them rather than to take away their jobs. Industry practices show that innovation in the driving environment is welcomed by the drivers, if it brings benefits or reduces their workload (TRIP, 2017). Second, similarly to the SARTRE and KONVOI projects, platooning should be tested more extensively on public roads in order to investigate how other traffic participants react to platoons. The general social acceptance should be evaluated and actions should be taken to facilitate the successful co-existence of automated driving trucks and passenger cars on the highways. The reaction of other road users should be examined in detail to guarantee that there are no safety risks for any of the parties. Then, traffic safety is also important, given that the European highway network has many access and exit points. Moreover, trucks should be equipped with special signs or signalling lights to increase their visibility and to communicate clearly to other road users which trucks are driving together in a platoon. Fortunately, steps in this direction have already been taken according to the EU Roadmap for Truck Platooning (Figure 12). Among other goals, the overall strategy includes a focus on platooning in real-traffic conditions, for example, to find out how other road users react to platoons and what is the optimal number of vehicles to operate jointly (ACEA, 2017d). Next, industry unions and regulators should think about the future when driving automation systems are able to maintain higher levels of automation. In this regard, there are still some open ethical issues, especially those related to the controversial way of how an autonomous driving vehicle responds to an emergency. Calculating how to respond in the case of hazardous situation means assigning value to the human life, which is sometimes a decision that must be taken in advance by a programmer. Normally, in case of any danger on the road, complex risk-benefit calculations are made by humans almost sub-consciously (Lipson & Kurman, 2016). Considering that self-preservation is an important aspect of the human nature, a potential problem for the manufacturer would be whether to build a vehicle that prioritizes the passengers and would be more demanded by customers, or one that would protect pedestrians instead. A study dealing with this problem is currently being conducted by the Massachusetts Institute of Technology. In this study, the so called MoralMachine application gathers opinions concerning the human perspective on moral decisions made by autonomous driving cars through simple use cases (MIT, 2017). For example, which option should a vehicle choose in a situation where it cannot avoid an upcoming accident but could possibly choose whose life to take. In the end, despite the undisputed benefits and live-saving potential of autonomous driving technology, the society needs to decide what rules and 85
regulations should govern these systems. Important for the future is that these decisions include accepted moral judgments, while still attract consumers to automation (Bonnefon et al., 2016). Finally, some experts do not preclude the possibility of harassment by other road users, just to test how automated trucks would react (Eckhardt, 2016a). Cut-ins might also happen without intention to disturb the platooning operation if, for instance, a road user tries to catch an exit in the last possible moment. Even though, from a technical point of view, a car slipping between platooning trucks would not pose a challenge, it is still risky if a truck tries to merge without noticing the platooning configuration. According to participants from the 2016 European Truck Platooning Challenge, the system automatically delinked the platoon whenever a single truck slipped between the platooning trucks (Eckhardt, 2016b). However, when the inter-vehicle distance was decreased to 0.5 seconds, other road users were more aware that the gap was insufficient to slip in between. In addition, if the traffic situation was very complex, the drivers decided by themselves to decouple the platoon. Nevertheless, drivers reported that other road users hesitated to conduct an overtaking manoeuvre and needed more time before deciding to start with it, or were simply irritated by the length of the platoon (Eckhardt, 2016b). Overall, the drivers share the opinion that truck platoons are potentially more problematic to the other road users than vice-versa. However, they stated that overtaking was only then an issue, when the truck platoon’s speed was not matching the speed of the surrounding traffic (Eckhardt, 2016b). To conclude, if the platoon’s speed and behavior is similar to the surrounding traffic, the probability of encountering problems with other road users is lower.
3.4 Legal considerations The following chapter deals with the barriers towards successful introduction of automated truck platooning from a legal and regulatory point of view. Historically, it is a well-known issue that legislation struggles to keep up with the pace of the technology advancements. Larry Downes’ law of disruption summarizes the problem perfectly by stating that “[…] the technology develops exponentially while social, economic and legal systems change incrementally” (Downes, 2009). A real-world example of this statement are the passenger seat belts – they were first offered as an option in 1949, but legislation in the United States did not require their presence in new cars until 1970 (Lipson & Kurman, 2016). How
regulatory issues can pose a barrier towards platooning introduction will be investigated in the following subchapters:
International regulatory action Regulatory constraints on automated vehicles
Regulatory constraints on drivers
Insurance, liability and certification
International regulatory action
Some of the most important global standards for automated vehicles have been provided by the Society of Automotive Engineers. According to SAE (2014), overall, a vehicle is a machine designated for transportation on public roads. Accordingly, a motor vehicle is one that is put in motion by mechanical power. The term comprises all vehicles designated for public roads except those engineered for rail infrastructure. Since the trend to test vehicles provided with driving automation systems on public roads is rather new phenomenon, special exemptions are needed to allow OEMs to conduct tests. At the same time, the general regulations are among the biggest barrier towards operation of driving automation systems. In the United States, for instance, NHTSA first issued several statements which imply that driving automation systems cannot be deployed on public roads for reasons other than testing (ATA, 2015). Likewise, the Intelligent Transportation Systems Joint Program Office has been established to conduct research and speed up adoption of driving automation systems and modern mobility concepts (Barbaresso et al., 2014). Its strategic plan is to achieve advancements in realizing automation and connected vehicle implementation by 2019. In the meantime, some state governments allowed testing of automated vehicles. The most advanced US state in this regard is California – so far, the state has issued several regulations and granted more than 30 licenses for testing (ATA, 2015). The certification process implies different certification activities and requires the testing party to be self-insured. Moreover, any malfunction of the driving automation system must be reported to the state. Other states, however, already have regulations about minimum following distances. Concerning truck platooning, this poses a barrier towards testing on public highways. However, NHTSA is expected to develop a safety standard related to vehicles with driving automation systems and to consider initiating
legislative changes in the definition of the term ‘driver’, in order that the latter is adapted to the current situation (Shladover, 2017). Similarly, regulatory changes are beginning to happen in Europe too. The Horizon 2020 Transport Work Programme is intended to support developments in automation and focuses on technologies such as ADAS, which will support the elimination of human error (ATA, 2015; EC, 2017). But even so, many European automotive manufacturers and suppliers are already developing ADAS solutions for passenger cars. Regarding ADAS in special, Horizon 2020 aims at supporting the progress of these developments and assuring that suitable standards and policies are available to the automotive industry. Moreover, international cooperation is encouraged to settle law standards regarding liability and to speed up the innovation process. Furthermore, in 2013 the International Road Transport Union published a document on automated driving that summarizes action points which needed to be addressed in favor of development of driving automation systems (ATA, 2015). The organization states that national and international road traffic conventions must be relaxed to allow testing of ADAS functions in real traffic conditions. Likewise, the organization points out that local authorities should monitor the development process closely but it will be better if this effort is synchronized on international level (IRU, 2010). In addition, further standards are needed to regulate V2V communication technology and to ensure that the systems are compatible among different brands. One further interesting point is that according to IRU (2010), retrofitting of trucks with sensor technology by companies other than OEMs should be promoted. However, installing new technology on an older truck must be approved and examined by local authorities to secure that all safety standards are met. Finally, local governments in Europe are increasingly showing interest in automated truck platooning and adapt their regulations accordingly. For example, Eckhardt (2016a) reports that tests of automated driving systems were permitted on public roads in the Netherlands, after a new law was effectively introduced on the 1st of July 2015. Additionally, the Netherlands Vehicle Authority (RDW) has been given the right to grant permissions for usage of innovative driving automation systems, as part of the overall goal to establish the Netherlands as a pioneer country in testing of automated vehicles (RDW, 2015). Moreover, the 2016 European Truck Platooning Challenge was the first breakthrough in bringing the topic of automated driving for discussion at highest political level. A coordinated approach can guarantee that a patchwork of regulations among EU member states is avoided and exemptions are provided by national road authorities (Eckhardt, 2016a). Eckhardt (2016b) points out that different national vehicle institutions 88
successfully managed the process of granting exemptions for allowing platooning trucks to take part in the European Truck Platooning Challenge (Eckhardt, 2016b). Moreover, Sweden, Denmark, Germany, Belgium and the Netherlands are among the European countries which have issued permits to OEMs to let them join the platooning event. Even though the permits differed in the requirements and specifications, they were an essential step in booking progress to provide successful amendment of national legislation for the needs of truck platooning. Nevertheless, limitations in some countries included aspects, such as bans on overtaking and driving on two-lane highways, or operation only on roads with an emergency lane. From a regulatory point of view, the three major risks during the experiment were the platoon’s length, the inter-vehicle distance, and the reliability of communication systems. Another interesting aspect of the challenge was the way how national vehicle authorities handled the risk mitigation process. Because the event was mostly perceived as a one-time demonstration, some countries relied on the truck manufacturers to analyse and report their plans for risk mitigation. The same were expected to bear the complete responsibility in the case of an accident. Then, some participating countries issued codes of practice with specific requirements to grant an exemption to the truck OEMs. Accordingly, this prescriptive approach is expected to become practice in the future, but since platooning is still in an initial stage of introduction, it is too early to define regulations that can fit everyone’s needs (Eckhardt, 2016b).
Regulatory constraints on automated vehicles
One of the most important documents posing a barrier towards operation of automated vehicles on Europe’s roads is the Vienna Convention (UNECE, 1968). In addition to this, the UNECE (United Nations Economic Commission for Europe) has set rules for the drivers and the maximum allowed speed in automated mode. Overall, the regulations state that the driver must constantly possess the ability to conduct maneuvers regarding vehicle speed and to monitor traffic in order to adjust the speed, or to stop if the situation requires to do so. Especially problematic regarding automated driving is ECE regulation 79, because ECE type approvals are needed for every vehicle to permit usage on public roads (Lutz, 2016). Regulation 79 stipulates that automation of steering functions is allowed only then, when the driver is primary in charge of the vehicle control. In addition, automated steering is limited to a speed of 10 km/h (Lutz, 2016), under the restriction that this is allowed only for parking or low speed maneuvers. The same regulation stipulates that the driver must have a superior
role over the system at any time, which clearly contradicts with the functionality of systems of higher SAE levels. The only exception is made for corrective action such as those of ESP (Electronic Stability Systems). Nevertheless, passive safety features such as ESP are engaged only over short amount of time, while in the meantime the driver keeps hands on the wheel. Similar barriers are set by ECE regulations 6 and 48, which say that indicators and directional signals must be operated only by the driver (Lutz, 2016). On the positive side, however, automated braking is not subject to any ECE restriction. In contrast, UNECE has recognized the need of amendment action and requirements on automatically commanded functions are expected to change soon. For example, the permission of automated steering up to a speed of 130 km/h and standardization of the behavior of other automated control functions are currently under discussion (Lutz, 2016). Next, the regulations on following distance also need to be harmonized across Europe. The best practice would be to centralize the efforts and let the European Commission set the rules together with industry organizations. In this case, substantial costs will be saved, because there will be no need for European truck manufacturers to meet regulations coming out of individual countries. More importantly, the border crossing prohibition of platooning trucks is essential for assuring that the highest potential of truck platooning can be achieved. For instance, if some European countries are not willing to amend their regulations as fast as needed, platoons might be forced to disconnect at the borders and trucks will have to proceed on their own. Indeed, cross-border operation is essential for truck platooning, because only then the hauliers can experience significant fuel savings and perceive platooning as advantageous. Since hauliers see the biggest advantage of platooning in deployment on longer distances, the policies from different countries should be aligned to fully utilize the potential of the new technology. To sum up, harmonization of regulations is needed and both national governments and the European Union will face a task of creating exemptions to permit testing on public roads and cross-border trials. Concerning the vehicle, the technical standards, the communication protocols, and the inter-vehicle distance are among those aspects that need to be adapted in the current regulations (Eckhardt, 2016a). As a first trial on public roads, the European Truck Platooning Challenge 2016 revealed some of the national vehicle regulator’s concerns (Eckhardt, 2016b). First, the length of the platoon has been constrained to ensure that it does not disturb the usual traffic flow and to avoid the risk of accidents when multiple trucks act as a single entity. Then, concerns related to road infrastructure were also reflected in the legal exemptions. For instance, the simultaneous usage of bridges from 90
several trucks was perceived as dangerous (Eckhardt, 2016b). Tunnels were also considered to be risky because of the likelihood that the communication system might be interrupted. For this reason, some of the participating countries banned platooning in tunnels for the time of the challenge. Next, the inter-vehicle gap was also regulated separately by every country – while some regulators prescribed exact meters or seconds, others had more relaxed requirements. Then, complex traffic situations were considered an issue regarding weather conditions and traffic density, but both aspects are not simple enough to be defined easily (Eckhardt, 2016b). Furthermore, attaching to and detaching from platooning trucks, as well as the actual platooning operation, were forbidden in some traffic situations which at that time were considered too risky. Finally, exemptions have been made regarding maximum speed, as local vehicle authorities put limitations between 80 and 90 km/h. Overall, it can be concluded that the European Truck Platooning Challenge 2016 revealed valuable insights into the potential processes and requirements that need to become practice as more automated driving trucks are deployed on the roads.
III. Regulatory constraints on drivers Among the regulations that limit the usage of driving automation systems are also those governing the driver’s role and responsibility. Even if an amendment succeeds in changing ECE regulation 79, the driver might be still required to supervise the system all the time. An example from the State of California, for instance, is a bill stipulating that the driver must remain attentive and able to regain control over the vehicle at any moment and, in addition, the driver is kept responsible for accidents (CA Gov, 2016). Moreover, Lutz (2016) argues that the overall impression from recent action of Germany’s Ministry of Justice and the rules used in California portend that the authorities are unwilling to let the driver conduct tasks other than driving and would rather require constant monitoring of the system. Yet, constant supervising might lead to much higher levels of fatigue and distraction, and can even increase the perceived workload. As has been mentioned before, another major obstacle towards letting the driver transfer the responsibility to the driving automation system is the Vienna Convention on Road Traffic (UNECE, 1968). The key part of the document that represents a problem is the one dedicated to road transport. It has been created long before driving automation systems were even planned. With regards to this, Article 8 of the Vienna Convention concerns different limitations related to the dynamic driving task. In short, the document stipulates
that every moving vehicle must have a driver, who should be able to control the vehicle at any time. Additionally, activities other than driving must be decreased to a minimum. How well Article 8 fits to today’s road traffic reality can be concluded by citing Sentence 5, which stipulates that “[…] every driver shall at all times be able to control his vehicle or to guide his animals”. Article 13, which governs how speed and distance shall be maintained, also indirectly points out that the driver should not conduct any additional tasks other than driving (Lutz, 2016). Clearly, the outdated articles of the Vienna Convention on Road Traffic are among the major barriers on the way to admission of automated truck platooning on public roads and automated vehicles in general. Nevertheless, in 2014 Austria, Belgium, France, Germany and Italy initiated an amendment and asked that an additional paragraph is added to facilitate introduction of vehicle automation. Although different paragraphs of the Vienna Convention contradict each other and lead to ambiguity in interpreting if the driver is allowed to take on a secondary role as opposed to the system, the final changes of the “5bis” paragraph that came in force on 23th of March 2016 (UNECE, 2014) have eventually settled this issue. In general, if the driving automation system is developed in synchrony to the ECE Regulations and can be overridden or switched off by the human driver, the automated vehicle is considered compliant to the Vienna Convention. Further proposals have been initiated by Sweden and Belgium with the aim of separating driving automation systems responsible for a portion of the dynamic driving task from those that are able to take over an entire journey from start to end. Concerning the driver, the daily allowance of driving time is also an aspect with importance to automated truck platooning. In the European Union, the amount of permitted driving time is indicated in the “European Agreement concerning the Work of Crews of Vehicles engaged in International Road Transport” (UNECE, 2006). This treaty imposes a maximum of nine hours driving time a day, with mandatory 45 minutes break in between. Although considering platooning, there is an opportunity for utilizing the driver in the following vehicle more effectively, only limited research with such focus can be found and no results from public road tests were identified. One can assume that if drivers are paid for a 10-hour working day including the brakes and are allowed to consume them during the time they are attached in a platoon, the total driving time will increase at least by 45 minutes, or to put it another way, with 8% a day (Janssen et al., 2015). Moreover, if regulators decide that the time spent in a following vehicle does not qualify as conducting a dynamic driving task, the total driving time a driver is allowed to cover daily might increase even more. In that way, longer trips and corresponding higher savings for hauliers will be possible. Further, 92
there are other regulatory barriers hindering the improved utilization of driving time. Among the essential hurdles in the European Union legislation are EC Regulation 561/2006, which enforces driver resting times, and the EEC Regulation 3821/85 for the digital tachograph – a device which records the activity of the driver while travelling (IRU, 2010). If a driver in following truck is not required to participate in the driving task, this time could be counted as driving time. In the ideal case, the driver of the following vehicle could be allowed to consume resting time while still being on the move in the vehicle, attached to the leading truck. Consequently, both driver and haulier can save valuable time during the workday, since the haulier avoids overtime payments and the driver is able to take a rest while still travelling. Nevertheless, this could not happen before EC 561/2006 is subject to amendment. What is more, EEC 3821/85 stipulates that the tachograph automatically switches on when the vehicle is moving, and counts this activity as driving time. Therefore, to increase the benefits of automated truck platooning, amendments are needed to add the possibility of recording resting instead of driving time, while the truck is in motion but the system and not the driver operates it. Under those circumstances, it is positive that the need for amending current regulations has already been addressed during the European Truck Platooning Challenge and truck manufacturers as well as regulatory organs are aware of the required changes. In addition, several organization such as the TNO are developing systems for truck matching that would also include driver resting time (TNO, 2017). In general, although not limited to Europe only, industry experts share the opinion that commercial vehicles are suitable for early adoption of driving automation systems with respect to driver regulations (ATA, 2015). Additionally, in April 2016 the “Amsterdam Declaration on cooperation in the field of connected and automated driving” (EU, 2016) has been signed by the transport ministers of the 28 EU member states. Being an important starting milestone as part of the EU Vision of Truck Platooning by 2025 (Figure 11) and the EU Roadmap for Truck Platooning (Figure 12), the Declaration of Amsterdam includes agreements on the further action needed to successfully develop automated driving technology in the European Union. In this document the Netherlands, the European Commission, all EU member states, national road authorities and automotive industry representatives agreed on creating common regulations that would subsequently let autonomous vehicles be deployed on public roads at least for testing purposes (EU, 2016). Thus, the Declaration of Amsterdam made a significant achievement in starting a discussion on higher political level concerning automated truck platooning. The effort to link developments of separate countries to a joint European approach paves the way 93
for cross-border platooning, once the regulations are amended and the technology proves to be safe.
IV. Insurance, liability and certification As has been noted before, platooning has the potential to greatly decrease accidents and human error. However, developing a 100% risk-free system is far from today’s reality. Accordingly, whenever an accident happens, it must be clear who bears the responsibility. According to NHTSA, the “National Motor Vehicle Crash Causation Survey” documented that between 2005 and 2007 94% of the road crashes were caused by the human driver (Singh, 2015). Therefore, chances are that insurance companies will face a complicated task in determining whose fault was the accident in the other 6% of the cases. This might be the manufacturer who assembled the vehicle, or also the supplier who developed the driving automation system. Moreover, considering that platooning will start as low-level automation technology, it is not trivial to distinguish if the driver or the system have caused an accident. In higher levels of automation, however, the driver would eventually be removed from the equation and the responsibility would probably shift to the parties involved in the production of the vehicle. Yet, from today’s point of view, only assumptions are possible. Once driving automation systems have started to be part of the series equipment of heavy trucks, insurance companies could gather enough data to analyse if the systems reduce the amount of accidents and to determine which party bears the liability. According to Californian insurance companies, the manufacturer of the vehicle fitted with a driving automation system should retain the liability (ATA, 2015). Still, this statement does not represent the view of the entire industry. Moreover, commercial and personal insurance differ regarding the discounts granted by insurers for having safety systems on board. In the future, if driving automation systems prove their ability to decrease accidents, hauliers could save insurance costs thanks to their improved accident record. Concerning liability, reports of the American Trucking Association reveal that today’s liability laws, at least in the USA, are suitable for deployment of automated truck platooning (ATA, 2015). The reports show that truck manufacturers are willing to bear the liability in the case of a crash, mostly because driving automation systems will be able to record data before and during the accident. Consequently, insurance companies can determine easily whose fault the accident was. According to Janssen et al. (2015) and Lipson & Kurman (2016), higher levels of automation featuring absence of a driver in the following
vehicle might pose a complex issue in determining which party is liable in the case of a crash. Possibilities range from the driver of the leading truck, through the truck OEM, to even the OEM’s supplier. While such a topic should be part of the contract between OEM and supplier, assigning fault could prove to be a difficult undergoing in the case of SAE level 1 and 2 systems. Eckhardt (2016a) mentions that liability could potentially shift from the driver to the truck manufacturer, but what raises more questions is the extent to which the leading vehicle is responsible for the following ones. In the case of an accident that involves the leading truck and has consequences for the following vehicles, clear regulations are required with respect to liability. What is more, the liability of the road authorities is also quite uncertain, because case laws involving automated driving are still scarce (Eckhardt, 2016b). The general expectation is that the road authorities care for the road’s condition and the driving automation system is engineered to comply with the usual road markings. In this case, the OEM would bear the risk of an accident. However, if a change in the road markings occurred and led to unexpected behavior of the automated vehicle, the road authorities might be held liable. Despite the plethora of potential benefits for society, platooning might pose a risk for the insurance businesses due to the decline in road crashes and the complex investigation of system fault. Finally, early forms of platooning might cause confusion on the insurance market and lead to higher insurance fees, which consequently could endanger the tight cost benefits resulting from fuel saving. To conclude, insurance companies would have to adapt their offers to the business case of automated truck platooning. Among the current ideas is that platoons are insured as a single vehicle entity before the diverse regulations are harmonized and advanced insurance models are created (Eckhardt, 2016a). In any case, a detailed analysis is needed. Similar issues are arising in relation to certification of trucks with driving automation functions. As of today, it is expected that certification is done by the OEMs and their suppliers in accordance to development standards that have been already discussed in previous chapters. A similar question is to be answered regarding the training of the truck drivers, where responsibility should be put either on industry or regulatory side. However, recommended practices issued by industry organizations would facilitate the process of setting common law and standards throughout the European Union and can help to settle the problematic aspects of insurance, liability and certification.
Consultation with industry partner
In parallel to the identification of various opportunities and barriers through a profound literature review, consultation with a representative from the road haulage industry was constantly taking place. Finally, the industry partner was asked for an opinion about the findings and the work has been concluded with an interview. The following chapters deal with the methodology and present the results of the cooperative work.
4.1 Approach First, an industry partner has been selected and asked to support the research for the thesis. Among the criteria was to choose an expert with knowledge about automated truck platooning, experience in the road freight transport business and readiness to implement the technology in its own fleet in the long run. During the research, Mr. Jan Verlaan, managing director at De Jong–Grauss Transport B.V., has been consulted on regular basis. De Jong–Grauss Transport is a road freight transport company specialized in container transport with more than 30 years’ experience. Its location near Rotterdam and primary operation area at the Port of Rotterdam corresponded with the focus of this thesis. Moreover, nowadays the Netherlands is among the European countries providing some the most favorable conditions for development of automated truck platooning. In addition, the company conducts international container transport and has experience with potential cross-border challenges regarding truck platooning. De Jong–Grauss Transport has participated in the 2016 European Truck Platooning Challenge in Rotterdam and works closely together with the local vehicle authorities and the TNO institute on issues related to truck platooning. Accordingly, the company supported this thesis with knowledge gained through first-hand experience and helped in identifying opportunities and barriers towards successful platooning implementation. What is more, De Jong–Grauss Transport B.V. is expected to participate in future road tests on automated truck platooning in the Netherlands, which once again emphasises the suitability of this industry partner to the research project.
Figure 25. Heavy cargo trucks are parked in front of the headquarters of De Jong–Grauss Transport B.V. in Rotterdam, the Netherlands (picture is provided through the courtesy of De Jong–Grauss Transport B.V.)
To acquire a first overview of the current automated truck platooning developments, the company’s headquarters (Figure 25) has been visited in April 2017. Throughout the research process, the company has been consulted on a regular basis. Upon finishing the literature review, an interview has been conducted in June 2017. With the insights collected during the communication with De Jong–Grauss Transport, the haulier’s perspective about automated truck platooning could be considered in detail. In the end, the hauliers and their drivers are those who will operate the trucks fitted with platooning technology and will encounter many of the challenges mentioned in this thesis during daily routines. The next chapters present the interview structure and a summary of the findings.
4.2 Interview In the first place, the summary of the identified opportunities and barriers has been presented to the interviewee (Table 4, p. 51). The findings originate from the literature review and were discussed with the industry partner in greater detail. Then, the industry partner has been interviewed using a set of prepared questions. Including both open and closed questions, the approach represents a mixture between a
structured and an unstructured interview. The aim of the questions was to examine the relevance of the literature review findings and to gather information for future academic research. The questions read as follows:
Which are the top three opportunities and benefits of automated truck platooning for the European road freight transport sector? Please name them and justify your opinion.
Which are the top three barriers towards successful introduction of automated truck platooning on public roads in Europe? Please name them and justify your opinion.
Considering the upcoming five years, which actions will be primarily needed to facilitate automated truck platooning operations?
Which aspect of automated truck platooning should be investigated primarily by future academic research: economic, technical, human, or legal? Please rate the four aspects with a grade from 1 to 5 according to their significance (1 for ‘not important at all’, 5 for ‘very important’).
What is your overall opinion on automated truck platooning?
The first two questions, together with the fourth question, are similar to those used in structured interviews. In addition, the interviewee has been asked to provide a short explanation of the answers to help understand the motivation of choosing one aspect of automated truck platooning over another. On the contrary, the third and the fifth questions are open questions. In this way, more flexibility was introduced and the interviewee was given the chance to speak about aspects of platooning that were potentially not identified during the literature review. The fourth question aimed at ordering the aspects that have been investigated in the thesis according to their importance and relevance for future investigation. In addition, the final question represented an opportunity to involve the interview partner into an open discussion and to gather insights into automated truck platooning from a more general point of view. 98
The data collected during the interview is meant to point out the direction of future academic research related to automated truck platooning. In the next chapter, the findings from the interview will be presented and discussed.
4.3 Discussion The interview with De Jong–Grauss Transport has been conducted in June 2017. The conversation comprised the five questions that have been presented previously, yet the discussed topics were not limited only to them. The interview outcome, presented in this chapter, is complemented by re-statement of relevant findings from the literature review.
Top three opportunities of automated truck platooning for the European road freight transport sector
Starting with the most important opportunities for the European road freight transport sector from haulier’s point of view, the reduction in fuel consumption and CO2 topped the list of benefits related to truck platooning. Improved road capacity and reduction of traffic jams came second, while the evolution of the driver’s profession has been put on third place, with a remark that changes related to it were not expected in the short run. As has been noted in the interview, from a haulier’s perspective any potential financial savings gained through technical innovation like platooning can be further invested in another innovation. Next, the reduction of greenhouse gas emissions is not less important than fuel savings because CO2 is already a great ecologic issue and is expected to become even more considerable in the future. Accordingly, if trucks consume less fuel, the positive impact will not be limited only to the industry, but will represent a benefit for the entire society (Verlaan, 2017). Nevertheless, despite the environmental benefits, the technology first needs to prove its economic feasibility to motivate the logistics businesses to invest. The second important opportunity on the list is the improvement of road capacity and decrease of traffic jams. Logistics business professionals share the opinion that if no measures are taken, the roads will be already overloaded in a few years (Verlaan, 2017). As has been mentioned in previous chapters, this statement is supported by academic research that reveals that automation of vehicles could contribute greatly to solving this issue (Fagnant & Kockelman, 2015). Still, although truck platooning can help to reduce traffic congestion, it is only one of the many measures which need to be implemented to achieve 99
improvements in road capacity. Other measures to increase efficiency include extended working times and operation of trucks overnight (Verlaan, 2017). Clients often have specific wishes regarding the time of delivery of the load, however, the businesses normally clock out in the late afternoon hours and the trucks stand still during the rest of the day. As a result, there is a huge improvement potential towards better road capacity utilization during the night hours, when the roads are only slightly occupied but, at the same time, the fleets of trucks are resting on their parking lots. The evolution of the driver’s profession comes third on the list of opportunities. As has been mentioned earlier, some reports foresee that the truck driver of the future is going to become a truck manager. While this scenario is still far away in time, logistics businesses already share the opinion that for this to become reality, the drivers need to undergo additional trainings. In the past, the job of the truck driver had less requirements compared to today’s standards. However, although the general driver’s skillset can improve over time, a certain maximum of capabilities cannot be exceeded (Verlaan, 2017). Because truck drivers are generally less educated compared to other professions, even if technology evolves, some 60-70% of the drivers are not expected to perform better than they already do today (Verlaan, 2017). On the other hand, the expected shortage of truck drivers is a fact, and measures are to be taken to handle the negative trends. Therefore, further action is needed to ensure that the drivers are trained enough to manage driving automation systems. It is not clear yet to which extent the drivers will be able to get accustomed to the new technology, thus, the only way to find out is to conduct more road tests and to analyze their behavior.
Top three barriers towards successful introduction of automated truck platooning on public roads in Europe
Next, the major barriers regarding automated truck platooning have been ranked as follows: technical and economic issues, regulatory action and legislation, insurance and liability issues. As has been stated before, there are several open questions regarding the road infrastructure, for example, the road junctions that are not designed for operation of platoons. Other examples include roundabouts and tunnels, but also bridges and highway exits. While these problems are likely to be solved by local vehicle and road authorities, the technical issues still cause uncertainty among the hauliers. From an operational point of view and considering safety, hauliers are unsure whether their drivers will be willing to drive in a
following truck and to trust the driving automation system, if they do not know the driver of the leading truck (Verlaan, 2017). In addition, the need for a camera mounted on the leading truck and a display in the cabin of each following truck is perceived as very important in order to raise the level of perception of the drivers and to let them know what is happening in front of the platoon. Moreover, logistics businesses are concerned with the reaction of the driving automation system in the event of emergency. The interviewee stated that, overall, the safety of the truck drivers has the highest priority (Verlaan, 2017). Also, the level of trust towards the system will play a major role in the implementation of automated driving. What is more, the sensor technology and software for automated truck platooning are still expensive. It is believed that with today’s prices the required total investment per truck accounts for around 40,000 Euro (Verlaan, 2017). At the same time, future large-scale production is expected to push the prices down to 10,000 or 11,000 Euro per truck (Janssen et al., 2015). Even so, considering a time horizon for return on investment, transport companies will need to operate many platoons to achieve break-even operations quickly enough. In addition, while the truck OEMs are expected to bear the costs during the testing phase, it is not clear yet what is going to be the exact price of the technology after the testing phase. In the end, only getting beyond the break-even point will not encourage transport companies to invest. The expected return on investment will be dependent on the market penetration of automated truck platooning, which conversely is related to the achievable fuel reduction – the higher the savings, the bigger the incentive for trucking companies to implement platooning. The second most important barrier towards platooning introduction is believed to be the international regulatory action, especially concerning cross-border transports. Although amendments of existing road transport regulations are already in progress and some local authorities such as those in the Netherlands are closely working with the industry to change the law and allow platooning tests, the synchronized international action seems to be more important for the platooning’s future than other aspects. From an economic point of view, automated truck platooning makes greater sense on longer distances. For smaller countries in the European Union such as the Netherlands or Belgium, long distances are hardly possible during domestic transport. Therefore, it is crucial for the hauliers that platoons can cross borders without the need to disconnect. Only in this way, the desired fuel savings can be achieved. The regulatory action needed in this regard is related again to economic considerations. According to the transport companies, fuel consumption accounts for approximately 25% of the total operating cost (Verlaan, 2017). Given this figure and 101
assuming that 10% fuel savings per kilometer are possible, the platoons will clearly profit more from longer routes across Europe that allow the trucks to maintain constant speed and inter-vehicle distance over a longer period of time. Currently, the savings vary between 3.5% and 5% fuel consumption reduction in real-road conditions (Verlaan, 2017). The 10% mark is still seen as optimistic, because those numbers were measured during tests that normally feature the best possible vehicle setup and environmental conditions. Even though, the opinion of the transport business is that if 10% fuel savings during international trips are feasible, an investment of 11,000 per truck is worthwhile (Verlaan, 2017). Furthermore, the efforts of Dutch vehicle authorities regarding longer heavier vehicles have proven that transportation innovations can successfully be tested on public roads. In the case of LHVs, which are six meters longer than a regular heavy cargo truck, road capacity is utilized better, the fuel consumption and CO2 emissions are decreased and more loads can be transported with one truck. However, LHVs are also not allowed to cross the border with any of the neighboring countries. To conclude, international regulatory action is among the top aspects that need special focus to improve the chances of automated truck platooning to gain success in the road transport sector. Number three in the group of barriers is yet another issue arising from the legal considerations. Insurance and liability are among those topics which are full of uncertainties. At the beginning, road tests with platooning-enabled trucks will be financed by the truck OEMs. Similarly, the insurance activities will be covered by the insurance businesses themselves. However, it is not clear what will happen after the testing phase is completed, because a lot of decisions rely on the findings of those tests. Insurance companies in the Netherlands, for example, still cannot answer the question about responsibility of the drivers in different positions in the platoon (Verlaan, 2017). Clearly, if the leading truck is involved in an accident, the likelihood that the following vehicles will be involved is very high. Yet, it is too early to say who is liable and to what extent every driver bears responsibility. The decisions about how to handle liability and insurance issues will be probably taken after sufficient number of road tests are done and, consecutively, will be affected by the number and severity of the accidents that have occurred in the meantime.
III. Primarily needed actions to facilitate automated truck platooning operations in the upcoming years When talking about the operational aspect of automated truck platooning, the first thing to be considered from the haulier’s point of view is the distribution of earnings. As has been mentioned previously, the TNO is currently investigating how to create a software program to automate this process. Nonetheless, the distribution of earnings is a complex issue. First, one should take into account how much every truck is saving per kilometer and equalize the profits throughout the platoon. Obviously, the leading truck will be the one to generate the least savings and those following closely behind will be in more favorable position. The system dealing with the distribution of profits might, for example, require following trucks to pay a charge to the leading one (Verlaan, 2017). To build a cost optimization model based on the distribution of earnings is, without a doubt, a complicated matter. There are many different factors playing a role, such as the technical specifications of the trucks, the timing of joining a platoon, and probably even the driving behavior. In this case, the existence of platooning service providers could be considered as a step forward in solving the issue. Nevertheless, more road tests are needed to conclude on how the system of profit distribution is going to look like. The second and third focus topics that will need further attention are the technical issues together with the problems related to road infrastructure, as well as insurance and liability problems. As an example, RWD, the road authority granting approvals of vehicles and components in the Netherlands is going to issue exemptions to allow some companies to test on public roads. Currently in the Netherlands, many parties are willing to conduct tests, yet, first and foremost the safety of the drivers and the other road users must be ensured (Verlaan, 2017). It is still unclear what results can be expected in the testing phase. Also, there is high uncertainty about how to deal with liability, if any accidents occur during testing. Given that from a technical point of view, the issues with truck safety can be cleared soon, it is still an open question how the liability topic will find a solution. While it is expected that future versions of platooning will give the drivers a chance to take hands off the wheel and conduct tasks other than driving, more tests are required to investigate the driver’s reaction in case of an accident. It is also controversial how a driver engaged in working with a tablet, for instance, can instantly return to the driving task if the system fails to deliver. Thus, industry professionals state that the number and type of accidents during the testing phase will be crucial for many aspects of future platooning implementation.
Nowadays, driving in a platoon is still considered illegal, and local vehicle authorities are those to grant permissions for testing. In fact, although transport companies are willing to test automated truck platooning technology in order be prepared for the future, safety is considered most important and first it should be ensured that drivers are protected to a sufficient extent. Overall, from an operational point of view, truck platooning does not pose a significant issue, however, legal and insurance problems are potential barriers that are still in clarification (Verlaan, 2017).
IV. Aspects of automated truck platooning to be investigated by future academic research As the industry partner stated, it is hard to say that one aspect is more important than the others: the economic, technical, human, and legal issues all have significant implications on truck platooning (Verlaan, 2017). Technical matters are important, especially concerning safety, legal barriers are to be overcome, human considerations regarding the drivers are not to be neglected and, finally, economic aspects such as return on investment should be considered too. Still, from the haulier’s point of view, three potential topics for future research have been noted during the interview:
Safety – Simulations are needed to model how accidents with platooning trucks occur and what impact they could have on the driver. In case of failure of the leading truck’s system, the reaction of the following trucks should be analyzed in detail. Also, it should be investigated how the interaction of driver and automated system works in real-time on public roads. All considerations should be done with a focus on achieving the highest safety and minimizing the risks for any traffic participant. These tests can be conducted on closed infrastructure, but the most significant information could be gathered only in real traffic, once the technical and legal aspects of platooning allow for this.
Economic feasibility – More research is needed to investigate the savings from fuel and the economic feasibility of automated truck platooning. Transport companies can be motivated by a positive business case that should consider not only the pure fuel reduction but also the effort to schedule platoons on-the-fly. According to the business, if a truck waits for more than 30 minutes to join a platoon and the savings are less than 2%, the economic feasibility will be gone. In the end, trucking companies would not
invest 11,000 Euro per trucks for a saving of only 1% or 2% of the total fuel consumption. Although practical tests promise cost savings reaching 10% and even 15%, the first large-scale practical tests will reveal to which extent those numbers are realistic. From a business point of view, getting beyond breakeven is essential. Therefore, future research can engage in investigating the business case of automated truck platooning for cross-border operation. 3.
Public reaction – Third on the list, the general public reaction finds a place among the priorities for future research. The reaction of other road users would be a game changer for automated truck platooning, if the society is not informed about automated truck platooning, how it works, and what its limitations are. It is not yet clear how other road users would react if they tried to exit a highway, but encountered a multiple-truck platoon on their side. Although some researchers state that platoons could easily handle a car slipping between the trucks, only practical testing can prove the actual safety levels. In any case, information campaigns should be conducted to avoid backlashing. In the end, if sufficient information is provided by the governments, the road users would rather adapt to the changes related to platooning (Verlaan, 2017). Still, additional research on the social aspects of automated truck platooning could contribute to successful implementation of public roads.
Overall opinion on automated truck platooning from the perspective of road freight business
According to De Jong–Grauss Transport, the industry has a long way to go before automated truck platooning is utilized in regular road transport operations. Moreover, even with advanced technology, the first and last kilometers of the job or, in other words, the time before joining and after leaving a platoon will remain a challenge to the driving automation systems (Verlaan, 2017). First, changes can start with exemptions granted by local authorities to those who want to test, but are also able to prove that they provide the required safety. Later, once testing has been completed successfully, automated truck platooning has the potential to become part of the daily business. Nevertheless, from the hauler’s perspective concepts such as LHV are more suitable in the short run. For example, testing of LHV in the Netherlands is over and several Scandinavian countries are utilizing them for years. With this, LHVs can be the first step of changing the road freight transport market in
Europe. However, LHVs are not perceived as competitive to platooning. In the long run platooning will be a logical step towards achieving autonomous driving and solving the issue with truck driver shortage. To sum up, automated truck platooning is not something that the business needs immediately, however, it will become important in the future. Accordingly, European hauliers are interested in testing of automated truck platooning as a matter of securing a competitive advantage in the long run. In the next decade, fully automated driving trucks will probably remain part of the operations in closed environments, such as terminal yards and mines, however automated truck platooning of lower SAE levels will be given a chance during road tests.
During the research, the opportunities and barriers regarding automated truck platooning have been successfully identified through a profound literature review and cooperation with an industry partner from the European road haulage sector. To summarize, platooning is an intermediate step in the incremental process of achieving full automation of commercial heavy trucks and has high chances of becoming the future of transportation in Europe. Consequently, automated truck platooning will bring changes in the future supply chains, however, its impact will not be limited to the road freight transport industry alone. For this reason, multidisciplinary approach is needed to ensure that various challenges concerning different aspects are considered during the development phase. From the haulier’s perspective, the decrease in fuel consumption and CO2 emissions tops the list of opportunities related to truck platooning. Then, the chance to reduce congestion and improve the utilization of public roads is placed second, followed by the expected changes in the driver’s profession. In contrast, the most significant barriers towards successful introduction of platooning were identified as concerns regarding the drivers’ safety, the unclear level of initial investment needed to acquire platooning technology for commercial usage and, finally, the uncertainty concerning the regulations about liability in case of an accident. In addition, industry experts state that platooning does not pose a problem from an operational point of view, however legislation issues concerning liability and distribution of earnings seems to be among the most complex problems that require more attention. From legal point of view, further collaborative action should be encouraged to harmonize international standards and regulations and, most importantly, to allow multibrand cross-border operation in Europe. Furthermore, it is advisable that future academic research is conducted with respect to the following aspects of automated truck platooning: safety, business case, and public reaction. While governments and industry organizations are already aware of the regulatory amendments needed to enable testing of driving automation systems, the level of safety concerning the drivers needs more attention. To achieve this goal, clear political support is needed to increase platooning testing on public roads. The same applies to offering benefits to early adopters such as lower taxes and incentives for the drivers. In addition, the road infrastructure should be updated to meet platooning requirements. Further tests and simulations are required to convince hauliers that trucks travelling at close distances are safe enough to let the driver take hands off the wheel and travel relying merely on the system. 107
Additionally, future research on automated truck platooning should consider the concept from a business perspective. A detailed analysis of the cost of technology and possible profits can explore whether investment in truck platooning equipment is feasible from economic point of view. In addition, suitable cost-sharing models for distribution of earnings along trucks from different fleets need to be implemented. Finally, additional research should assess the purely human-related factors concerning the operating of trucks fitted with driving automation systems. On the one hand, the possible implications and behavior of the drivers need special attention. On the other hand, the potential resistance from other road users should be investigated too. The public opinion can be tested through surveys and information campaigns to assess how people react to truck platoons. The findings can be then considered in the further technology development. Finally, joint research projects can engage more in showcase activities to present cross-border and multi-brand platooning concepts to the various stakeholder groups in order to encourage further mutual collaboration. To conclude, automated truck platooning can be advantageous for the road freight transport sector in Europe. However, road tests with automated trucks should be continued in real traffic conditions. Only then potential opportunities and barriers from various nature can be examined in greater detail. At the end, there is an evident need for further academic research on any of the different aspects mentioned in this thesis.
References Aarts, L., & Salet, M. (2012). Experiences with LHV's in the Netherlands: Typical Dutch or valid for Europe. Conference proceedings of the 12th International Symposium on Heavy Vehicle Transport Technology 2012. Retrieved May 8, 2017, from https://trid.trb.org/view.aspx?id=1366966 ACEA (2009). Weight and dimensions of heavy commercial vehicles as established by Directive 96/53/EC and the European Modular System (EMS). Retrieved May 5, 2017, from https://ec.europa.eu/transport/sites/transport/files/modes/road/events/doc/2009_06_ 24/2009_gigaliners_workshop_acea.pdf ACEA (2017a). Facts about the automobile industry. European Automobile Manufacturers Association. Retrieved May 4, 2017, from http://www.acea.be/automobileindustry/facts-about-the-industry ACEA (2017b). Factsheet trucks. European Automobile Manufacturers Association. Retrieved April 20, 2017, from http://www.acea.be/uploads/publications/factsheet_trucks.pdf ACEA (2017c). Truck industry gears up for wide-spread introduction of semi-automated convoys by 2023. European Automobile Manufacturers Association. Retrieved May 15, 2017, from http://www.acea.be/press-releases/article/truck-industry-gearsup-for-wide-spread-introduction-of-semi-automated-conv ACEA (2017d). EU truck platooning roadmap. European Automobile Manufacturers Association. Retrieved May 15, 2017, from http://www.acea.be/uploads/publications/Platooning_roadmap.pdf ACEA (2017e). Trucks, vans and buses. Retrieved May 2, 2017, from http://www.acea.be/automobile-industry/trucks-vans-buses Adaptive (2015). Deliverable D2.1: "System classification and glossary". Adaptive Consortium. Retrieved April 18, 2017, from https://www.adaptiveip.eu/files/adaptive/content/downloads/Deliverables%20&%20papers/AdaptIVeSP2-v12-DL-D2.1%20System%20Classification.pdf Adolfson, M. (2016). COMPANION – An overview and final demonstration. COMPANION Homepage. Retrieved April 21, 2017, from http://www.companionproject.eu/wp-content/uploads/20160915-COMPANION-Overview-Day2.pdf Akerman, I., & Jonsson, R. (2007). European modular system for road freight transport experiences and possibilities. TransportForsK AB. Retrieved May 15, 2017, from http://www.modularsystem.eu/download/facts_and_figures/20080522att01.pdf Alam, A. (2014). Fuel-efficient heavy-duty vehicle platooning (Doctoral dissertation). KTH Royal Institute of Technology. Retrieved April 7, 2017, from https://pdfs.semanticscholar.org/983b/08e97fc88d10bc0dac1444b5318c1baa6d93.p df Alam, A., Besselink, B., Turri, V., Martensson, J., & Johansson, K. (2015). Heavy-duty vehicle platooning for sustainable freight transportation: A cooperative method to enhance safety and efficiency. IEEE Control Systems, 35(6), 34-56. Alvarez, L., & Horowitz, R. (1997). California Partners for Advanced Transit and Highways (PATH): Safe platooning in automated highway systems. University of California at Berkeley. Retrieved April 29, 2017, from http://www.its.berkeley.edu/sites/default/files/publications/UCB/97/PRR/UCBITS-PRR-97-46.pdf Anderson, J., Nidhi, K., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Autonomous vehicle technology: A guide for policymakers. Santa Monica,
California: RAND Corporation. Retrieved April 15, 2017, from http://www.rand.org/content/dam/rand/pubs/research_reports/RR400/RR4432/RAND_RR443-2.pdf ATA (2015). Automated driving and platooning: Issues and opportunities. ATA Technology and Maintenance Council. Retrieved April 8, 2017, from https://www.google.at/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja& uact=8&ved=0ahUKEwjz8vnCw6TVAhXFXhoKHfslDFYQFggtMAA&url=http% 3A%2F%2Forfe.princeton.edu%2F~alaink%2FSmartDrivingCars%2FITFVHA15 %2FITFVHA15_USA_FutureTruck_ADP_TF_WhitePaper_Draft_Final Audi (2014). Audi media center: Piloted driving. Retrieved April 4, 2017, from https://www.audi-mediacenter.com/en/piloted-driving-3651 Bakermans, B. (2016). Truck platooning - Enablers, barriers, potential and impact. Delft Universitiy of Technology. Retrieved April 30, 2017, from https://repository.tudelft.nl/islandora/object/uuid%3A3e4e265b-b84c-474b-85b99840a5c85464 Barbaresso, J., Cordahi, G., Garcia, D., Hill, C., Jendzejec, A., & Wright, K. (2014). USDOT’s intelligent transportation systems (ITS): Strategic Plan 2015- 2019. US Department of Transportation. Retrieved April 19, 2017, from https://www.its.dot.gov/strategicplan.pdf Bergenhem, C., Huang, Q., Benmimoun, A., & Robinson, T. (2010). Challenges of platooning on public motorways. 17th World Congress on Intelligent Transport Systems, (pp. 1-12). Retrieved May 13, 2017, from http://www.transportresearch.info/sites/default/files/project/documents/20130204_115543_96121_SAR TRE_ConferencePaper1.pdf Bergenhem, C., Shladover, S., Coelingh, E., Englund, C., & Tsugawa, S. (2012). Overview of platooning systems. Proceedings of the 19th ITS World Congress. Vienna, Austria. Retrieved May 6, 2017, from http://publications.lib.chalmers.se/records/fulltext/174621/local_174621.pdf BESTFACT (2015). BESTFACT Best practice case quick info - Urban freight. Best Practice Factory for Freight Transport. Retrieved May 15, 2017, from http://www.bestfact.net/wpcontent/uploads/2016/01/CL1_109_QuickInfo_GOFER-16Dec2015.pdf Bevly, D. (2014). Driver assistive truck platooning (DATP): Evaluation, testing, and stakeholder engagement for near-term deployment. Auburn University. Retrieved April 20, 2017, from http://vra-net.eu/wpcontent/uploads/2014/04/Driver_Assisted_Truck_Platooning_DATP_FHWAAuburn.pdf Bevly, D., Murray, C., Lim, A., Turochy, R., Sesek, R., Smith, S., & Smith, N. (2015). Heavy truck cooperative adaptive cruise control: Evaluation, testing, and stakeholder engagement for near term deployment (Phase one final report). Auburn University. Retrieved April 22, 2017, from http://eng.auburn.edu/~dmbevly/FHWA_AU_TRUCK_EAR/FHWA_AuburnDAT P_Phase1FinalReport Bimbraw, K. (2015). Autonomous cars: Past, present and future. A review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO) (pp. 191-198). IEEE. Blanco, M., Atwood, J., Vasquez, H., Trimble, T., Fitchett, V., Radlbeck, J., . . . Morgan, J. (2015). Human factors evaluation of level 2 and level 3 automated driving
concepts. National Highway Traffic Safety Association (NHTSA). Retrieved April 29, 2017, from https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/812043_hfevaluationlevel2andlevel3automateddrivingconceptsv2.pdf Bonnefon, J., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573-1576. Bonnet, C., & Fritz, H. (2000). Fuel consumption reduction in a platoon: Experimental results with two electronically coupled trucks at close spacing. SAE technical paper. Retrieved May 7, 2017, from http://papers.sae.org/2000-01-3056/ Broughton, J., & Walter, L. (2007). Trends in fatal car accidents: Analysis of CCIS data. Crowthorn: TRL Limited. Retrieved May 15, 2017, from https://trl.co.uk/reports/PPR172 Browand, F., McArthur, J., & Radovich, C. (2004). Fuel saving achieved in the field test of two tandem trucks. California Partners for Advanced Transit and Highways (PATH). Retrieved May 13, 2017, from http://its.berkeley.edu/sites/default/files/publications/UCB/2004/PRR/UCB-ITSPRR-2004-20.pdf Buehler, M., Iagnemma, K., & Singh, S. (2009). The DARPA urban challenge: Autonomous vehicles in city traffic (Vol. 59). Springer. CA Gov. (2016). Order to Adopt Title 13, Division 1, Chapter 1 , Article 3.7 – Autonomous vehicles. State of California Homepage. Retrieved May 28, 2017, from https://www.dmv.ca.gov/portal/wcm/connect/d48f347b-8815-458e-9df25ded9f208e9e/adopted_txt.pdf?MOD=AJPERES Caltagirone, L., Torabi, S., & Wahde, M. (2015). Truck platooning based on lead vehicle speed profile - Optimization and artificial physics. 18th International Conference on Intelligent Transportation Systems (pp. 394-399). IEEE. Chen, Q., Ozguner U., & Redmill, K. (2004). Ohio state university at the 2004 Darpa Grand Challenge: Developing a completely autonomous vehicle. IEEE Intelligent systems, 19(5), 8-11. COMPANION (2014). COMPANION: Road trains on track. COMPANION Consortium. Retrieved April 25, 2017, from http://www.companion-project.eu/launch-of-localcompanion-site/ Costello, B., & Suarez, R. (2015). Truck driver shortage analysis. American Trucking Association (ATA). Retrieved May 1, 2017, from http://www.trucking.org/ATA%20Docs/News%20and%20Information/Reports%20 Trends%20and%20Statistics/10%206%2015%20ATAs%20Driver%20Shortage%2 0Report%202015.pdf Cotrell, N., & Barton, B. (2013). The role of automation in reducing stress and negative affect while driving. Theoretical Issues in Ergonomics Science, 14(1), 53-68. Coulon Cantuer, M. (2016). COMPANION final event 14th & 15th September 2016. Update on recent developments related to connected and automated driving. European Commission. Retrieved March 30, 2017, from http://www.companionproject.eu/wp-content/uploads/20160914-COMPANION-Key-Note-M.Coulon.pdf Cunningham, M., & Regan, M. (2015). Autonomous vehicles: human factors issues and future research. Proceedings of the 2015 Australasian Road Safety Conference. Retrieved April 29, 2017, from http://acrs.org.au/files/papers/arsc/2015/CunninghamM%20033%20Autonomous% 20vehicles.pdf DAF (2015). DAF and TNO demonstrate ‘EcoTwin’. DAF Homepage. Retrieved April 17, 2017, from https://www.daf.com/en/news-and-media/articles/global/2015/q1/2703-2015-daf-and-tno-demonstrate-ecotwin#
Daimler (2015). World premiere on U.S. highway: Daimler Trucks drives first autonomous truck on public roads. Retrieved May 3, 2017, from http://media.daimler.com/marsMediaSite/en/instance/ko/World-premiere-on-UShighway-Daimler-Trucks-drives-first-autonomous-truck-on-publicroads.xhtml?oid=9919773 Daimler (2016). The PROMETHEUS project launched in 1986: Pioneering autonomous driving. Daimler AG Homepage. Retrieved May 10, 2017, from http://media.daimler.com/marsMediaSite/en/instance/ko/The-PROMETHEUSproject-launched-in-1986-Pioneering-autonomous-driving.xhtml?oid=13744534 Daimler (2017a). Connected Trucks: Freight transport of the future by using the internet. Daimler Homepage. Retrieved May 2, 2017, from https://www.daimler.com/innovation/digitalization/connectivity/connectedtrucks.html Daimler (2017b). Platooning: birds as a role model. Daimler. Retrieved April 12, 2017, from https://www.daimler.com/innovation/next/platooning-birds-as-a-rolemodel.html Davila, A. (2013). Report on fuel consumption. SARTRE, Deliverables. Retrieved April 30, 2017, from http://www.sartre-project.eu/en/publications/ DB Schenker (2017). Networked trucks: DB Schenker and MAN intensify their partnership for autonomous driving. DB Schenker Homepage. Retrieved May 2, 2017, from https://www.dbschenker.com/global/about/press/networked-trucks--dbschenker-and-man-intensify-their-partnership-for-autonomous-driving-8154 De Bruijn, F., & Terken, J. (2014, November). Truck Drivers as Stakeholders in Cooperative Driving. European Conference on Ambient Intelligence (pp. 290-298). Springer International Publishing. Deutschle, S., Kessler, G., Lank, C., Hoffmann, G., Hakenberg, M., & Brummer, M. (2010). Use of electronically linked konvoi truck platoons on motorways. ATZautotechnology, 20-25. Dixit, V., Chand, S., & Nair, D. (2016). Autonomous vehicles: disengagements, accidents and reaction times. PLoS one, 11(12). Retrieved May 2, 2017, from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168054 Downes, L. (2009). The laws of disruption: Harnessing the new forces that govern life and business in the digital age. Basic Books. EC (2012). Road Transport - A Change of Gear. European Commission. Retrieved May 19, 2017, from https://ec.europa.eu/transport/sites/transport/files/modes/road/doc/broch-roadtransport_en.pdf EC (2017). Horizon 2020, Work Programme 2016-2017 - 11. Smart, Green and Integrated Transport. European Commission. Retrieved March 14, 2017, from http://ec.europa.eu/research/participants/data/ref/h2020/wp/2016_2017/main/h2020 -wp1617-transport_en.pdf Eckhardt, J. (2016a). European truck platooning challenge 2016 - creating next generation mobility. Storybook. The Hague, the Netherlands: Rijkswaterstaat. Retrieved May 16, 2017, from https://www.eutruckplatooning.com/support/storybook/handlerdownloadfiles.ashx? idnv=501163 Eckhardt, J. (2016b). European truck platooning challenge 2016 - creating next generation mobility. Lessons learnt. Rijkswaterstaat. Retrieved May 16, 2017, from https://www.eutruckplatooning.com/Support/Booklet+Lessons+Learnt/default.aspx
Eilers, S., Martensson, J., Pettersson, H., Pillado, M., Gallegos, D., Tobar, M., & Adolfson, M. (2015). COMPANION--Towards Co-operative Platoon Management of HeavyDuty Vehicles. 18th International Conference on Intelligent Transportation Systems (ITSC) 2015 (pp. 1267-1273). IEEE. Endsley, M. (2012). Designing for situation awareness: An approach to user-centered design. CRC Press. EU (2016). Declaration of Amsterdam. Cooperation in the field of connected and automated driving. The Netherlands EU Presidency 2016 Homepage. Retrieved May 17, 2017, from https://english.eu2016.nl/binaries/eu2016en/documents/publications/2016/04/14/declaration-of-amsterdam/2016-04-08declaration-of-amsterdam-final-format-3.pdf EU Truck Platooning Challenge (2016). EU Truck Platooning Challenge. Press photos. Retrieved April 10, 2017, from https://www.eutruckplatooning.com/Press/Photos+Volvo/default.aspx EUCAR (2017). The Automotive Industry: Focus on Future R&D Challenges. European Council for Automotive R&D. Retrieved May 10, 2017, from http://www.eucar.be/wp-content/uploads/2015/01/EUCAR-FOCUS-2009_Web.pdf Eureka (2017). Programme for a european traffic system with highest efficiency and unprecedented safety. Retrieved May 15, 2017, from http://www.eurekanetwork.org/project/id/45 European Commission (2017). Automotive Industry Overview. European Commission. Retrieved May 10, 2017, from https://ec.europa.eu/growth/sectors/automotive_en Eurostat (2017). Road Freight Statistics 2017. Retrieved May 1, 2017, from http://ec.europa.eu/eurostat/statisticsexplained/index.php/Freight_transport_statistics Fagnant, D., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167-181. Fernandes, P., & Nunes, U. (2012). Platooning with IVC-enabled autonomous vehicles: Strategies to mitigate communication delays, improve safety and traffic flow. IEEE Transactions on Intelligent Transportation Systems, 13(1), 91-106. Finnerty, J. (2017, March 30). Auto Express. Retrieved April 14, 2017, from autoexpress.co.uk: http://www.autoexpress.co.uk/car-tech/85183/driverless-carseverything-you-need-to-know-about-autonomous-vehicles Flament, M. (2016). Truck Platooning. Next Steps. European Commission. Retrieved May 2, 2017, from http://www.companion-project.eu/wp-content/uploads/20160914COMPANION-Key-Note-M.-Flamant.pdf Fritz, H. (1999). Longitudinal and lateral control of heavy duty trucks for automated vehicle following in mixed traffic: experimental results from the CHAUFFEUR project. Control Applications, 1348-1352. Gasser, T., & Westhoff, D. (2012). BASt-study: Definitions of automation and legal issues in Germany. Proceedings of the 2012 Road Vehicle Automation Workshop. Retrieved April 26, 2017, from http://onlinepubs.trb.org/onlinepubs/conferences/2012/Automation/presentations/G asser.pdf Gasser, T., Arzt, C., Ayoubi, M., Bartels, A., Bürkle, L., Eier, J., . . . Vogt, W. (2013). Legal consequences of an increase in vehicle automation. German Federal Highway Research Institute. Retrieved April 26, 2017, from http://bast.opus.hbznrw.de/volltexte/2013/723/pdf/Legal_consequences_of_an_increase_in_vehicle_au tomation.pdf
GCC (2017). SARTRE project completes first successful on-road demo of multiple vehicle platooning. Retrieved April 4, 2017, from http://www.greencarcongress.com/2012/01/sartre-project-completes-firstsuccessful-on-road-demo-of-multiple-vehicle-platooning.html Gilbertsen, C. (2017). Here's how the sensors in autonomous cars work. www.thedrive.com. Retrieved April 20, 2017, from http://www.thedrive.com/tech/8657/heres-how-the-sensors-in-autonomous-carswork Goel, A., & Vidal, T. (2013). Hours of service regulations in road freight transport: an optimization-based international assessment. Transportation science, 48(3), 391412. Grahamslaw, T., & Marsh, P. (2016). Platooning: the implications on operations, maintenance and driver behaviour. Conference 2016 Association for European Transport (AET). Retrieved April 14, 2017, from https://abstracts.aetransport.org/paper/download/id/5080 Green Car Congress (2016). Six automated truck platoons to compete in European Truck Platooning Challenge. www.greencarcongress.com. Retrieved April 21, 2017, from http://www.greencarcongress.com/2016/03/20160325-platooning.html Heidmann, M. (2012). Der Gigaliner-Chancen, Risiken und Zukunftspotenziale in Deutschland und Europa (In German). GRIN. Henning, K., Petry, L., Ramakers, R., & Meinhold, D. (2009). Sustainable transport— Knowledge and innovations at RWTH Aachen university for Europe's systems of tomorrow. IEEE International Conference on Systems, Man and Cybernetics (pp. 2403-2408). SMC 2009. HERE (2017). HERE HD Live Map: The most intelligent vehicle sensor. Retrieved April 15, 2017, from https://here.com/en/products-services/products/here-hd-live-map Hetzner, C. (2016). BMW's innovation, adaptability will be tested in the 21st century. Automotive News. Retrieved May 4, 2017, from http://www.autonews.com/article/20160307000100/OEM/303079999?template=pri nt Humphreys, H., Batterson, J., Bevly, D., & Schubert, R. (2016). An evaluation of the fuel economy benefits of a driver assistive truck platooning prototype using simulation (No. 2016-01-0167). SAE Technical Paper. Retrieved May 19, 2017, from http://papers.sae.org/2016-01-0167/ IKA (2017). Research projects from A to Z: KONVOI. Institute for Automotive Engineering RWTH Aachen University. Retrieved April 20, 2017, from https://www.ika.rwth-aachen.de/en/research/projects/driver-assistance-vehicleguidance/1636-konvoi.html Ioannou, P. (2013). Automated highway systems. Springer Science & Business Media. IRU (2010). IRU response to the public consultation on the revision of the community legislation on the recording equipment in road transport (Tachographs). International Road Transport Union. Retrieved April 29, 2017, from https://www.iru.org/sites/default/files/2016-03/iru-response-to-the-publictachograph.pdf IRU (2017). Driver shortage problem. International Road and Transport Union. Retrieved April 23, 2017, from https://www.iru.org/what-we-do/network/driverportal/problem ITF (2017). Managing the transition to driverless road freight transport. Case-specific policy analysis. Paris: International Transport Forum - OECD. Retrieved June 15,
2017, from https://www.itf-oecd.org/sites/default/files/docs/managing-transitiondriverless-road-freight-transport.pdf Janssen, R. (2017). Truck platoon matching paves the way for greener freight transport. TNO. Retrieved April 28, 2017, from https://www.tno.nl/en/focusareas/urbanisation/mobility-logistics/reliable-mobility/truck-platoon-matchingpaves-the-way-for-greener-freight-transport/ Janssen, R., Zwijnenberg, H., Blankers, I., & de Kruijff, J. (2015). Truck platooning: Driving the future of transportation. Delft: TNO. Retrieved 1 April, 2017, from https://repository.tudelft.nl/view/tno/uuid:778397eb-59d3-4d23-9185511385b91509/ Jaynes, N. (2016). Here's the timeline for driverless cars and the tech that will drive them. mashable.com. Retrieved April 18, 2017, from http://mashable.com/2016/08/26/autonomous-car-timeline-and-tech Jiang, T., Petrovic, S., Ayyer, U., Tolani, A., & Husain, S. (2015). Self-driving cars: Disruptive or incremental. Applied Innovation Review(1), 3-22. Konecranes. (2017). Automated guided vehicles. Retrieved April 24, 2017, from http://www.konecranes.com/equipment/container-handling-equipment/automatedguided-vehicles Kunze, R., Ramakers, R., Henning, K., & Jeschke, S. (2009). Organization and operation of electronically coupled truck platoons on German motorways. International Conference on Intelligent Robotics and Applications (pp. 135-146). Springer Berling Heidelberg. Liang, K., Martensson, J., & Johansson, K. (2013). When is it fuel efficient for a heavy duty vehicle to catch up with a platoon? IFAC Proceedings Volumes, 46(21), 738743. Lipson, H., & Kurman, M. (2016). Driverless. Intelligent cars and the road ahead. Cambrige, Massachusetts: The MIT Press. Lockridge, D. (2013). Coming to a highway near you: Driverless vehicles. truckinginfo.com. Retrieved April 20, 2017, from http://www.truckinginfo.com/blog/all-thats-trucking/story/2013/09/coming-to-ahighway-near-you-driverless-vehicles.aspx Lodovici, M., Pastori, E., Corrias, C., Sitran, A., Tajani, C., Torchio, N., & Appetecchia, A. (2009). Shortage of qualified personnel in road freight transport. European Parliament, Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies, Transport and Tourism. Retrieved March 18, 2017, from http://www.europarl.europa.eu/RegData/etudes/etudes/join/2009/419101/IPOLTRAN_ET(2009)419101_EN.pdf Lu, X. Y., & Shladover, S. E. (2011). Automated truck platoon control. California Partners for Advanced Transportation Technology (PATH). Retrieved April 28, 2017, from http://escholarship.org/uc/item/7c55g2qs Lutz, L. (2016). Automated vehicles in the EU: Proposals to amend the type approval framework and regulation of driver conduct. General Reinsurance Corporation. Retrieved May 28, 2017, from http://media.genre.com/documents/cmint16-1-en.pdf Martinez, L., Kauppila, J., & Castaing, M. (2014). International freight and related CO2 emissions by 2050: A new modelling tool. International Transport Forum 2014. OECD. Retrieved April 2, 2017, from https://www.itfoecd.org/sites/default/files/docs/dp201421.pdf
May, J., & Baldwin, C. (2009). Driver fatigue: The importance of identifying casual factors of fatigue when considering detection and countermeasure technologies. Transportation Research part F: traffic psychology and behavior, 12(3), 218-224. McBride, J., Ivan, J., Rhode, D., Rupp, J., Rupp, M., Higgins, J., & Eustice, R. (2008). A perspective on emerging automotive safety applications, derived from lessons learned through participation in the darpa grand challenges. Journal of Field Robotics, 25(10), 808-840. McKinnon, A. (2009). Innovation in road freight transport: Achievements and challenges. Innovation in Road Transport: Opportunities for Improving Efficiency. Lisbon, Portugal. Retrieved May 6, 2017, from https://trid.trb.org/view.aspx?id=1149671 McKinsey&Company (2016a). Automotive revolution – perspective towards 2030. How the convergence of disruptive technology-driven trends could transform the auto industry. Retrieved April 1, 2017, from https://www.mckinsey.de/files/automotive_revolution_perspective_towards_2030.p df McKinsey&Company (2016b). Delivering change – The transformation of the commercial vehicle sector up to 2025. Retrieved March 10, 2016, from www.mckinsey.de: https://www.mckinsey.de/files/mck_delivering_change.pdf Mentzer, J., DeWitt, W., Keebler, J., Min, S., Nix, N., Smith, C., & Zacharia, Z. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1-25. Michael, J., Godbole, D., Lygeros, J., & Sengupta, R. (1998). Capacity analysis of traffic flow over a single-lane automated highway system. Journal of Intelligent Transportation System, 4(1-2), 49-80. Milanes, V., Shladover, S., Spring, J., Nowalkowski, C., Kawazoe, H., & Nakamura, M. (2014). Cooperative adaptive cruise control in real traffic situations. IEEE Transactions on Intelligent Transportation Systems, 15(1), 296-305. MIT (2017). MoralMachine - Human perspectives on machine ethics (MIT research project). Massachusetts Institute of Technology Website. Retrieved February 15, 2017, from http://moralmachine.mit.edu/ Mobileye (2017). Our technology. Mobileye Homepage. Retrieved May 13, 2017, from http://www.mobileye.com/our-technology/ Negnetievsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson Education. NHTSA (2013). Preliminary statement of policy concerning automated vehicles. Washington DC: National Highway Traffic Safety Administration. Retrieved April 28, 2017, from https://www.nhtsa.gov/staticfiles/rulemaking/pdf/Automated_Vehicles_Policy.pdf NLA (2017). Driver Shortage in the North? Nordic Logistics Association. Retrieved May 20, 2017, from http://nla.eu/focus-area/driver-shortage/ Norman, D. (2009). The design of future things. Basic Books. Nowalkowski, C., Shladover, S., & Tan, H. (2015). Heavy vehicle automation: Human factors lessons learned. Procedia Manufacturing, 3, 2945-2952. Nunen, E., Tzempetzis, D., Koudijs, G., Nijmeijer, H., & van den Brand, M. (2016). Towards a safety mechanism for platooning. Intelligent Vehicles Symposium, 502507. Oliver Wyman (2016). A driverless future for freight. Retrieved May 15, 2017, from Oliver Wyman: http://www.oliverwyman.com/content/dam/oliverwyman/global/en/2016/may/owtl/07-A-Driverless-Future-for-Freight.pdf Panteia (2014). Ex-post evaluation study report. Study on the effectiveness and improvement of the EU legislative framework on training of professional drivers.
European Commission. Retrieved March 11, 2017, from https://ec.europa.eu/transport/sites/transport/files/factsfundings/evaluations/doc/2014_ex_post_evaluation_study_training_drivers_en.pdf PATH (2017). California PATH homepage: Publications. California Partners for Advanced Transportation Technology (PATH) . Retrieved April 22, 2017, from http://www.path.berkeley.edu Ploeg, J., Serrarens, A., & Heijenk, G. (2011). Connect & Drive: design and evaluation of cooperative adaptive cruise control for congestion reduction. Journal of Modern Transportation, 19(3), 207-213. Rauch, N., Kaussner, A., Krueger, H., Boverie, S., & Flemisch, F. (2009). The importance of driver state assessment within highly automated vehicles. 16th ITS World Congress, Stockholm, Sweden, 21, 25. RDW (2015). European Truck Platooning Challenge 2016 booklet. The Netherlands Vehicle Authority, RDW. Retrieved May 2, 2017, from https://www.eutruckplatooning.com/support/handlerdownloadfiles.ashx?idnv=4376 26 Renesas (2017). Advanced Driver Assistance System (ADAS). Retrieved May 11, 2017, from https://www.renesas.com/en-in/solutions/automotive/adas.html Robinson, T., Chan, E., & Coelingh, E. (2010). Operating platoons on public motorways: An introduction to the SARTRE platooning programme. 17th World Congress on Intelligent Transport Systems, 1, 12. Roeth, M. (2013). CR England & Peloton Technology: Platooning test November 2013. North American Council for Freight Efficiency. Retrieved April 20, 2017, from http://nacfe.org/wp-content/uploads/2013/12/CR-England.pdf Ross, D. (1998). Competing through supply chain management: Creating market-winning strategies through supply chain partnerships. Chicago, Illinois: Springer Science & Business Media. SAE (2013). Truck platoon demo reveals 15% bump in fuel economy. SAE International. Retrieved April 21, 2017, from http://articles.sae.org/11937/ SAE (2014). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE On-Road Automated Vehicle Standards Committee. Retrieved April 2, 2017, from http://standards.sae.org/j3016_201609/ SAE (2017). Truck platoon demo reveals 15% bump in fuel economy. SAE International. Retrieved April 24, 2017, from http://articles.sae.org/11937/ SARTRE (2016). Summary of SARTRE Publications and Deliverables. www.sartreproject.eu. Retrieved April 29, 2017, from https://www.sp.se/sv/index/research/dependable_systems/Documents/The%20SAR TRE%20project.pdf Schaller, R. (1997). Moore's law: Past, present and future. IEEE Spectrum, 52-59. Schmitz, J. (2004). Energy-economic cost-benefit-analysis of electronically coupled truck convoys (Diploma thesis). Aachen, Germany: RWTH Aachen University. Schulze, M. (1997). CHAUFFEUR-The European way towards an automated highway system. Mobility for everyone. 4th world congress on intelligent transport systems. Berlin. Retrieved April 22, 2017, from https://trid.trb.org/view.aspx?id=539633 Shladover, S. (1978). Longitudinal control of automated guideway transit vehicles within platoons. Journal of Dynamic Systems, Measurement, and Control, 100(4), 302310. Shladover, S. E. (2017). Road vehicle automation: Let's get real. Berkeley: California PATH Program. Retrieved May 14, 2017, from
http://www.path.berkeley.edu/sites/default/files/my_folder_76/Pre_03.2017_Road Vehicle.pdf Shladover, S. E., Lu, X. Y., Song, B., Dickey, S., Nowakowski, C., Howell, A., . . . Nelson, D. (2006). Demonstration of automated heavy-duty vehicles. California Partners for Advanced Transit and Highways (PATH). Retrieved May 12, 2017, from http://escholarship.org/uc/item/6kc7m4jr Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey (No. DOT HS 812 115). Washington: NHTSA. Retrieved April 12, 2017, from https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812115 Stanton, N., & Young, M. (2005). Driver behaviour with adaptive cruise control. Ergonomics, 48(10), 1294-1313. Stein, G. (2006). Mobileye Technologies Limited. System and method for detecting obstacles to vehicle motion and determining time to contact therwith using sequences of images. United States. Retrieved May 29, 2017, from https://www.google.com/patents/US9096167 Sugimachi, T., Fukao, T., Suzuki, Y., & Kawashima, H. (2013). Development of autonomous platooning system for heavy-duty trucks. IFAC Proceedings Volumes, 46 (12), pp. 52-57. The Economist (2016). High definition maps: The autonomous car’s reality check. Retrieved April 14, 2017, from The Economist: http://www.economist.com/news/science-and-technology/21696925-buildinghighly-detailed-maps-robotic-vehicles-autonomous-cars-reality Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., & Lau, K. (2006). Stanley: The robot that won the DARPA Grand Challenge. Journal of field Robotics, 23(9), 661-692. TNO (2017). Truck matching enables greener transport. TNO - Netherlands Organisation for Applied Scientific Research. Retrieved April 28, 2017, from https://time.tno.nl/en/articles/truck-matching-enables-greener-transport/ Torrey, I., Ford, W., & Murray, D. (2014). An analysis of the operational costs of trucking: 2014 Update. Arlington, United States: American Transportation Research Institute. Retrieved May 4, 2017, from http://www.atri-online.org/wpcontent/uploads/2014/09/ATRI-Operational-Costs-of-Trucking-2014-FINAL.pdf TRIP (2017). CHAUFFEUR II: Project details. Transport Research & Innovation Portal. Retrieved May 5, 2017, from http://www.transport-research.info/project/promotechauffeur-ii Tsugawa, S. (2014). Results and issues of an automated truck platoon within the energy ITS project. Intelligent Vehicles Symposium Proceedings (pp. 642-627). IEEE. Tsugawa, S., Jeschke, S., & Shladover, S. (2016). A review of truck platooning projects for energy savings. IEEE Transactions on Intelligent Vehicles, 1(1), 68-77. UNECE (1968). Convention on road traffic - Done at Vienna on 8th November 1968. Amendment 1*. United Nations Economic Commission for Europe. Retrieved May 9, 2017, from https://www.unece.org/fileadmin/DAM/trans/conventn/crt1968e.pdf UNECE (2006). European agreement concerning the work of crews of vehicles engaged in International Road Transport (AETR). Geneva: United Nations Economic Commission for Europe Inland Transport Committee. Retrieved April 20, 2017, from https://www.unece.org/fileadmin/DAM/trans/doc/2010/sc1/ECE-TRANSSC1-2010-AETR-en.pdf UNECE (2007). Regulation No. 51 of the Economic Commission for Europe of the United Nations (UN/ECE) — Uniform provisions concerning the approval of motor
vehicles having at least four wheels with regard to their noise emissions. Economic Commission for Europe of the United Nations (UN/ECE). Retrieved May 3, 2017, from http://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX:42007X0530(02) UNECE (2011). UNECE works on new standards to increase the safety of trucks and coaches. UNECE. Retrieved April 9, 2017, from http://www.unece.org/press/pr2011/11trans_p10e.html UNECE (2014). Report of the sixty-eighth session of the working party on road traffic safety. United Nations Economic and Social Council. Retrieved May 11, 2017, from https://www.unece.org/fileadmin/DAM/trans/doc/2014/wp1/ECE-TRANSWP1-145e.pdf Urmson, C. (2015). Chris Urmson: How a driverless car sees the road. TED Talks (Youtube). Retrieved February 22, 2017, from https://www.youtube.com/watch?v=tiwVMrTLUWg Vasseur, H., Pin, F., & Taylor, J. (1992). Navigation of car-like mobile robots in obstructed environments using convex polygonal cells. Robotics and autonomous systems, 10(2-3), 133-146. Verlaan, J. (2017). Interview about opportunities and barriers of automated truck platooning with Jan Verlann from De Jong-Grauss Transport BV. Notes can be obtained from the author. Interview conducted by the author on 14.06.2017. Vis, I. (2006). Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research, 170(3), 677-709. Volvo (2012). Volvo Car Corporation concludes following the SARTRE project: Platooned traffic can be integrated with other road users on conventional highways. Retrieved April 24, 2017, from https://www.media.volvocars.com/global/engb/media/pressreleases/45734 Volvo Trucks (2017). Volvo trucks successfully demonstrates on-highway truck platooning in California. Volvo Trucks Homepage. Retrieved March 18, 2017, from https://www.volvotrucks.us/about-volvo/news-and-events/volvo-truckssuccessfully-demonstrates-on-highway-truck-platooning-in-california/ Wille, M., Röwenstrunk, M., & Debus, G. (2007). KONVOI: Electronically coupled truck convoys. HFES-EC Conference on Human Factors for Assistance and Automation. Braunschweig, Germany. Retrieved May 9, 2017, from http://www.hfeseurope.org/wp-content/uploads/2014/06/19-fa.pdf Wolters, P. (2016). European Truck Platooning Challenge (pilot) 2016. Brussels, Belgium: European Shippers' Council. Retrieved May 15, 2017, from http://europeanshippers.eu/wp-content/uploads/2016/04/ESC_Truckplatooning_final.pdf Yoshida, J., Sugimachi, T., Fukao, T., Suzuki, Y., & Aoki, K. (2010). Autonomous driving of a truck based on path following control. Retrieved May 12, 2017, from https://www.jstage.jst.go.jp/article/kikaic/77/783/77_783_4125/_article Zhang, W., Misener, J., & Chan, C. (2014). Feasibility assessment of a truck automation deployment framework. Paper presented at 2014 Transportation Research Board Annual Meeting. Retrieved May 19, 2017, from https://trid.trb.org/view.aspx?id=1289965