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VIGILANCE REVIEW Driver Role in Automated Highway Systems (AHS)

March 31, 1997

by Michael E. McCauley, Ph.D. James C. Miller, Ph.D. Thomas J. Sharkey, MA Monterey Technologies, Inc. 987 University Ave., Suite 12 Los Gatos, California 95030 Phone (408)354-3149

Prepared for: Federal Highway Administration Joint Project Office Intelligent Transportation Systems Contract No. DTFH-94-C-00067 for delivery to the NAHSC

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Monterey Technologies, Inc.

PREFACE The Intelligent Transportation System (ITS) initiative is a major national program to increase the efficiency and safety of transportation through advanced technology. The National Architecture was developed by two contractor teams with several other organizations providing support to the Government lead entity, the Federal Highway Administration’s Joint Program Office (JPO). Monterey Technologies, Inc. (MTI) participated in that program as a representative of the National Architecture Technical Review Team (TRT) Specifically, Dr. Michael McCauley of MTI was named as a member of the TRT to provide background and experience in the area of human factors. After several years of periodic analysis and review, the TRT completed it’s final program review in 1996. The JPO asked members of the TRT to provide continued technical input to the FHWA ITS programs. In that context, MTI was asked to review the scientific literature on the topic of “vigilance” to assist the Driver Role Committee of the National Automated Highway System Consortium (NAHSC) in their analyses of the potential roles to assign the driver in a AHS. Thank you to Dr. Robert Hogan of the NAHSC, head of the Driver Role Committee, for providing the guidance for this review.

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TABLE OF CONTENTS PREFACE ………………………………………………………

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INTRODUCTION ………………………………………………

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BACKGROUND …………………………………………… OVERVIEW OF DRIVER TASKS ……………………………

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METHODS ………………………………………………………

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VIGILANCE REVIEW …………………………………………

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DEFINITION ……………………………………………… TASK TERMINOLOGY ……………………………………. ENGINEERING DATA COMPENDIUM ……………………… Warm (1984) …………………………………………… Parasuraman (1987) …………………………………….. Haber and Hershonson (1980) ………………………….. Huey and Wickens (1993) ……………………………… Sheridan (1987) ………………………………………… Kantowitz and Sorkin (1987) ……………………………

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REFERENCES…………………………………………………

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OTHER OBSERVATIONS ……………………………………

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APPENDIX A: VIGILANCE RESEARCH AND HIGHWAY VEHICLE AUTOMATION ……………………

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INTRODUCTION BACKGROUND The ITS program was instituted on the basis of achieving 29 user services. Several of these user services combine to form a cluster of technologies and capabilities known as Automated Vehicle Control Systems (AVCS) or the Automated Highway System (AHS). The principal concept unifying this subset of ITS is to automate one or more aspects of vehicle control, in an effort to increase vehicle throughput and highway capacity. Secondary benefits would be to reduce driver stress and strain by freeing the driver from the responsibility for continuous closed-loop control of the vehicle. Examples of the functions that conceptually are candidates for automation include:  longitudinal control  lateral control  collision avoidance These automated control functions could be exercised either in dedicated AHS lanes or in mixed traffic, sometimes called “free agency,” where both manual and automated vehicles would share the same lanes. In dedicated lanes, groups of vehicles, sometimes called “platoons,” could be spaced closely together, with as little as one meter separation and vehicle speeds could be on the order of 90 miles per hour. There are some challenging human factors issues that must be addressed to develop a successful AHS program. Key human factors issues include careful consideration of:  driver role(s) at various stages of automation  transition procedures into and out of AHS lanes  driver-vehicle interface design (information display & controls)  driver monitoring or other techniques for ensuring control transfer  driver responsibilities in emergency conditions  user acceptance, system security, and safety These issues are particularly challenging because of the wide range of driver characteristics in the general population. Drivers are a far more diverse population, for example, than commercial pilots, who have faced a similar evolution in cockpit automation. A ground transportation trip, from a user-centered perspective, can be thought of as comprising the following elemental functional categories:  destination selection  mode selection (auto, bus, light rail)  route selection and rerouting  navigation  vehicle control  lateral-- lane keeping, lane selection/changes, turns  longitudinal-- speed selection & control, headway selection & control

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maintain safety margin & legal limits

Because of advances in technology, many of these functions can be accomplished either by a the human driver or by automation. Function allocation between human and machine is an important step in human-systems engineering. The ITS National Architecture exhaustively enumerated the functions and information flow between entities in both the Logical Architecture and the Physical Architecture, but it stopped short of function allocation between human users and the system. This is not a shortcoming of the ITS Architecture but a consequence of the difference between architecture and system design. In a successful usercentered system design, the driver’s roles, responsibilities and tasks must be established across all scenarios, modes of operation, and special (emergency) situations. This is the domain of the Driver Role team of the NAHSC.

OVERVIEW OF DRIVER TASKS One category of human behavior that has engendered considerable research attention since World War II is called vigilance. At least two of the roles that drivers could potentially play in an Automated Highway System are in the realm of behaviors subject to the phenomenon called “vigilance decrement,” i.e., the decrease in the probability of signal detection as a function of time. Brief descriptions of these two driver roles, which are not necessarily exclusive or independent, are as follows: Role 1: maintain vigilant search for potential hazards, while vehicle control is fully automated. Role 2: monitor the “health” and performance of the automated system. The purpose of this review is to identify empirical research and relevant articles in the fields of experimental psychology and human factors/ergonomics that provide insight into the efficacy of assigning “vigilance” roles to the driver in an Automated Highway System. To enable a focused analysis of the vigilance literature, a brief analysis of the driver tasks associated with these roles is needed. Establishing this context will facilitate estimates of the applicability of research results to the AHS situation. For “Role 1” above, the driver is “hands-off” and “feet-off” the controls, but is engaged in visual search through the windshield for potential objects that could pose a hazard. This role requires target detection, although the nature of the target is not specifically defined. That is, the target could be any number of animate or inanimate objects of varying size, distance, mass, velocity, and so on. The driver’s cognitive processing requirements go beyond detection and recognition to include classification as to whether or not the target poses a hazard. If it does, it can be classified as a potential “obstacle.” Thus, the driver’s role is more complex and requires more stages of processing compared to laboratory target detection tasks in which the characteristics of the target are well defined in advance. To fulfill this role,

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the driver must successfully execute the following perceptual and cognitive tasks within a limited time: 1. 2. 3. 4.

detect a potential target correctly classify it as an obstacle (safety hazard) select an appropriate evasive maneuver or other response successfully execute the evasive maneuver without endangering other traffic.

A problem exists if the driver’s performance is insufficient in any of the following ways:  fails to detect a target  false alarm-- the driver detects a target, falsely classifies it as a hazard and makes an unnecessary evasive maneuver  an evasive maneuver is poorly chosen or executed (even with correct detection and short RT)  response time (RT) is too long (even though the target is detected and classified) For Role 2 above, the driver is hands- and feet-off controls, monitoring relevant sources of information as to the acceptable performance of the automated system. This role is classified as a “supervisory control” task (Sheridan, 1972), because the driver is out of the direct control loop and assigned to an outer-loop of monitoring the performance of the inner loop. The driver may engage in the following tasks: 1. 2. 3. 4.

scan in-vehicle displays for status indicators, velocity, and other data scan outside features such as lane markers distance to lead vehicle make judgments as to the acceptability of vehicle position & dynamics if out of tolerance, take manual control or input data to make adjustments

A problem exists if the driver’s performance is insufficient in any of the following ways:  fails to detect in-vehicle display that is out-of-limits  fails to detect relevant information from outside vehicle  makes incorrect judgment of acceptability of vehicle position and dynamics  judges it acceptable when it is not  judges it unacceptable when it is within limits  fails to take corrective action when needed With this context of driver behavior in two potential roles in AHS, we can proceed to an overview of the vigilance research and related areas such as automation, function allocation, supervisory control, workload transition, fatigue, and human-computer monitoring.

METHODS This review employed multiple approaches in an attempt to achieve convergence of results. Several library database searches were executed, based on key words. This technique was used in multiple searches both on the DIALOG ™ system and on the World Wide Web. A 6

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“hand” search was conducted in corporate and university libraries. Key individuals, such as Dr. Parasuraman and Dr. Wiener, were contacted by telephone to gain their insight and opinion on important concepts and key articles. This is not necessarily an exhaustive review. It is intended to be sufficient to provide guidance with respect to decisions about driver role in AHS. The applicability of laboratory research findings is a matter of professional opinion. The opinions expressed herein are those of the authors and do not necessarily reflect the opinion of the FHWA, the ITS Architecture Technical Review Team, or the NAHSC. This draft report is a compilation of the materials we have gathered to date. It suffers from lack of organization and coherent structure. The final version, hopefully, will remedy those shortcomings, plus add several significant publications (books) that are not covered in this draft report, in particular, Engineering Psychology and Human Performance (1992) by Wickens, and Automation and Human Performance (1996) by Parasuraman and Mouloua.

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VIGILANCE REVIEW DEFINITION Vigilance tasks involve detecting infrequent, simple signals over prolonged periods of time without rest. Vigilance is a state of readiness to detect these signals and involves sustained attention (Boff and Lincoln, 1988). TASK TERMINOLOGY: VIGILANCE, SEARCH, AND MONITORING Vigilance tasks usually are characterized by temporal uncertainty, but not spatial uncertainty. Search tasks, by contrast, involve signals that have high spatial uncertainty, but little or no temporal uncertainty. Monitoring tasks require the detection of signals having both temporal and spatial uncertainty. In some monitoring tasks, the signal is not precisely specified, but must be inferred and may change over time. Note that the latter definition aptly describes the obstacle detection task in AHS.

ENGINEERING DATA COMPENDIUM Perhaps the single best source of information on vigilance for the applied behavioral scientist or engineer is the vigilance section of the Engineering Data Compendium by Boff and Lincoln (1988). The following information has been abstracted from that source. With advances in technology and automation, the role of the human operator has changed from that of an active controller to a decision maker and manager (a shift from active to supervisory control.) The problems of vigilance and monitoring occur when the automated system malfunctions or some unusual but infrequent condition occurs. The application of signal detection theory to vigilance performance enabled performance to be separated into two factors—observer sensitivity and criterion (bias) shift. Vigilance Decrement When averaged over 30-min blocks of trials, correct detection rate shows the greatest drop between the first and second blocks (in the first 30 min). Knowledge that the task is long contributes to an early and steep vigilance decrement. [see figures]. Response Time Three general findings: (1) false alarms are slower than hits, misses, or correct rejections; (2) positive responses (its and false alarms) become slower as time on task increases; (3) negative responses (misses and correct rejections) become faster with time on task. [all based on procedure with 2-4 signals/min].

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Signal Detection Theory & Vigilance Signal detection theory provides the means to account for the role of decision processes in detection situations (Swets, 1977). Signal strength alone does not determine observer performance. The observer’s likelihood of responding (yes or no) depends not only on sensitivity, but on placement of the decision criterion. The criterion may be moved independently of signal strength. Correct detection rate, the measure of vigilance performance used in early studies, is inadequate for two reasons: (1) the same percentage of “Hits” can occur with either a high or low rate of incorrect detections, (2) The hit rate reflects both the detectability of the signal (observer sensitivity) and the response bias (decision criterion) of the observer. The Receiver Operating Characteristic (ROC) curve is a graphic depiction of hit and false alarm probability pairs. In an ROC analysis of vigilance decrement, the ROC can be used to determine whether a given change in performance results from a change in detectability (sensitivity) or in the decision criterion, or both. ----------------------------------------------Figure of ROC curve p.1509 goes here -----------------------------------------------

AHS Notes: the nature of the ROC curve(s) for obstacle detection is important for the analysis of the driver’s role in detecting potential obstacles. The signal strength will depend on the size, distance, spatial frequency, and other optical characteristics of the potential obstacle as well as signal duration and other factors. Perhaps a tractable subset of categories of obstacles could be defined, such as: Animals: deer, large dogs, horses, other People/pedestrians Transportation objects: Autos, motorcycles, bicycles Misc. Objects: barrels, cement blocks, metal or wood objects of size >X Signal Characteristics Signal strength, frequency, and probability affect the rate of detection. With respect to signal probability, both real and expected probabilities have effects. Real probability is learned over the task, on the basis of the experienced event rate. Higher probabilities yield a greater level of detection and less vigilance decrement. For the AHS obstacle detection task, the signal rate and probability are extraordinarily low, relative to the signal rates used in vigilance research, i.e., on the order of one event per quarter or per annum for AHS versus several to many events per hour in vigilance research. Thus,

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the expected signal rate over time in AHS will be very low resulting in low probability of detection as well as large vigilance decrement. Auditory signals yield better signal detection performance than visual signals in the vigilance paradigm. For AHS, this argues against assigning the driver the role of visual obstacle detection. It is compatible with the design concept of using an auditory alerting signal based on detection of potential obstacles by radar, FLIR, or other sensors. Task complexity influences vigilance performance. A detection task yield better vigilance performance than a task requiring estimation of magnitude or other post-detection decisions. Again, the AHS obstacle detection task would be considered “complex,” because it involves object identification, location, motion and hazard assessment. Therefore, this type of task is particularly susceptible to vigilance decrement. (Schoonard, Gould, and Miller, 1973). Signal Target Location This information is based on signal detection from a radar scope and the relevance to the AHS task is uncertain. However, the results from the radar task indicate that the probability of detection is greater toward the center of the display than in the periphery. It is likely that drivers in AHS will have a tendency to look forward in the direction of travel with some saccadic eye movements to the road side as objects capture visual attention. This type of visual pattern is likely to support detection of obstacles on or near the driver’s current path. The probability of detection is uncertain for more peripheral objects moving on a collision course with own-vehicle. Perhaps these issues of visual scan behavior and the probability of correct obstacle detection could be addressed in research, using either a high fidelity visual driving simulator or under controlled conditions on a test track. Simultaneous Visual and Auditory Monitoring Detection performance is better with simultaneous visual and auditory signals than with either modality alone. In AHS, obstacle detection is assumed to be visual only. Observer Characteristics Young adults perform better than older adults. There is a wide variation based on individual differences. Practice Effect Hit rate increases and false alarm rate decreases with practice. Vigilance decrement still occurs within sessions. Effect of Instruction Instructions can influence criterion (B).

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Effects of Training Practice, instructions, and knowledge of results (KR) can influence signal detection performance. Effect of Boredom Observers who report high boredom after one hour have significantly greater response times (detection latency). Sex Differences Male/female differences in vigilance performance are very small. Males may have a slightly higher target detection rate compared to females. Intertask Correlations Factors affecting vigilance performance are numerous and include differences in visual search tasks and signal characteristics, such as duration, pacing, and event rate. End Engineering Data Compendium ---------------------------------------------------------

Warm, J.S. (Ed.) (1984). Sustained Attention in Human Performance. New York: John Wiley & Sons. Machines can no longer be considered as the willing but witless servants of human operators. Instead, as a result of automation, they have become our “partners” and vigilant behavior is an important element in the contribution to this partnership. (p.9). The operator’s role has evolved along more “executive” lines in which much time is spent in the passive monitoring of [system information] for occasional ‘critical’ stimuli that demand decision and action. Viewed in the context of an automation-oriented society, in which failures to detect critical signals can often be disastrous, the problem of vigilance assumes considerable significance. The [vigilance]decrement function bears witness to the fact that, by placing monitoring responsibilities primarily in the hands of the human component of man-machine systems, we may have created work situations for which people are not ideally suited. Temporal Uncertainty The overall likelihood of detection is markedly affected by the density of critical signals. Many studies have demonstrated that the probability of detection varies as a function of signal density ----------------------------------------11

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Figure on log signal rate, p. 30 -----------------------------------------The probability of critical signals also has an influence on the latency of detection. In general, response time for signal detection varies inversely with signal probability. The implications of these findings are not good for the driver role of obstacle detection in AHS. Because obstacles will be rarely encountered in AHS, relative to the signal rates used in the vigilance research, both the probability of detection and the response times are likely to be poor. Driving Applications Brown (1979) points out that very easy tasks, such as driving in low density traffic, may lead to low levels of arousal, with the following consequences: (1) The driver may not be able to cope with an emergency when it occurs (2) The driver may be induced to engage in irrelevant activities to counteract the monotony of the task. Brown cautions against simply making the task easier, which has all too often been a goal of human factors scientists [and systems engineers]. Note that is advice is given from the perspective of normal, manual-mode driving, but it may be relevant to the potential problem of arousal and vigilance for the AHS driver who is given the role of obstacle detection or the supervisory control task of monitoring the functioning of the automated systems. After a critical review of vigilance research intended to be relevant to driving, Warm (1984) states, “What remains to be shown is whether or not conventional vigilance findings would apply to the real driving task-- to the detection of unexpected signals appearing in the roadway, a pedestrian dart-out, for example.” This is a reasonably good description of the AHS obstacle detection role. In short, Warm is saying that we don’t know for certain how the classical vigilance research applies to this task. We agree with Warm’s conclusion that addressing these issues in simulation is a practical, safe approach to the problem. In discussion of the relevance of vigilance research to aviation, Warm makes an interesting point with relevance to the driver role in AHS. “The [aviation}task is not free of Type I errors. [One] collision between two airliners…was due, in part, to a visual illusion which led one flight crew to think that a collision was imminent, and take erroneous evasive action that actually brought the two aircraft together.”

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A similar scenario is logically possible in AHS -- a driver falsely concludes that a collision is imminent, takes manual control and executes a clumsy evasive maneuver that results in a crash; one that would not have occurred under automated control. “Alerts” are not a Panacea According to Warm, “An alerting device is nothing more than a substitute vigilance task (an infrequent signal with temporal uncertainty). Warning devices merely trade one vigilance problem for another.” One problem is the frequency of spurious alerts (false alarms) which lead to operators ignoring the alerts. Despite Warm’s warning, the aviation community has routinely adopted both auditory and visual cautions and warnings in the cockpit for a variety of unusual or important situations. While alerts are not a panacea, they can enhance safety if they are well designed, i.e., they have good operator interface characteristics and a low probability of Type I and II errors. ----------------------------------------Parasuraman, R. (1987) Human-Computer Monitoring. Human Factors, 29(6), 695706. The introduction of automation technology has changed both the nature of work and the type of perceptual and cognitive demands imposed on human users. Humans and computers often have to serve as joint monitors of events, processes, or entire systems. Human monitoring performance can be suboptimal as a result of lowered vigilance. One of the principal issues to be considered in a decision of what functions should be automated is the impact of automation on human attentional capabilities; for example, the ability of human operators to monitor system failures or emergency events in a highly automated environment. A potential problem is that the human operator may be required to detect an infrequent but critical condition or the failure of the automated system itself. The changed role of the human operator from active controller to passive monitor has not eliminated the vigilance problem, but merely changed it. The role of attention and vigilance in the new era of automation is thus both similar to and more complex than that in earlier technological eras. (p.698) A distinction must be drawn between vigilance effects related to decrements over time versus those associated with the overall level of performance. Even if a vigilance decrement is not found…, a vigilance problem may still exist if the level of vigilance is low and suboptimal. Computer assistance to improve monitoring performance is feasible, but a reliable improvement in efficiency-- as opposed to criterion shifts-- is not always obtained. (p. 703). Although joint human-computer monitoring performance is better than either human or computer performance alone, computer assistance is most beneficial for difficult signals and if training is provided. (p.703)

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Parasuraman gives a short review of the concept of optimal workload, particularly with reference to cockpit automation. Chambers and Nagel (1985) concluded that total automatic control was not desirable and that “partially autonomous” cockpits would be safer and more reliable. Two opposing design concepts are (a) fully automatic, in which the pilot would play a role in passive monitoring of the system, and (b) pilot’s assistant, in which routine tasks are automated, but the human pilot carries out most of the planning and procedural tasks. It should be noted, however, that the optimal function allocation in the commercial cockpit may differ from that in AHS because there are substantial differences in task complexity, user/operator selection, training and qualification.

Haber, R.N. and Hershenson, M. (1980), The Psychology of Visual Perception. New York: Holt, Rinehart and Winston. Studies of visual target detection have attempted to define the “useful field of view” as the areas surrounding the fixation point from which the perceiver can detect and process a signal. Mackworth (1976) had subjects monitor two displays for the movement of small circles and an occasional square. Detection of the squares in the peripheral window depended on how far into the periphery the second window was placed. The following graph shows the results. ----------------------------------------------Figure 16-1 Peripheral Target Detection -----------------------------------------------A large drop off in the probability of detection is seen with increases in the angular distance between the point of fixation and the peripheral display. The probability of detection drops from nearly 100% at 2 deg. separation, down to approximately 10% detection at 14 deg. separation. With respect to AHS, it is likely that the same general shape of function would occur, that is, higher probability of target detection for straight ahead. However, the absolute values of the function may differ from Mackworth’s data because of the differences between the experimental task and the AHS task.

OTHER SOURCES “Narrowing of attention” is discussed by Davies and Tune (1969) as the tendency for peripheral signals to be ignored, particularly at the end of a long spell at work. There is evidence that narrowing of attention is present in vigilance situations. One hypothesis is that

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effective scanning behavior diminishes over time. This would be consistent with the finding that signal detection performance is better when potential signals are clustered in one area rather than widely distributed spatially. Other studies have found corroborating evidence indicating that response time is increased for spatially distributed signals as compared to centrally located displays. Brown (1979) states that, “the majority of road accidents are attributable to human errors in perception and decision making…” (p. 109). Attention and effort (Kahneman, 1973) relate to arousal and above and below some optimal level of arousal, performance deteriorates. This implies that making tasks easier may be counter-productive for safety. If tasks are made too easy, individuals may have difficulty in sustaining alertness. Brown concludes, “we have to avoid simply making the road user’s task easier, and thus producing an undesirably low level of arousal and attention” (p. 115). While these comments were made with respect to normal driver-in-the-loop operation, they may have implications for the driver role in AHS. If Brown is correct in these assertions, then it is unlikely to expect meaningful contributions from AHS drivers for whom a very low level of arousal, attention, and effort would be required under normal circumstances.

Huey, B.M. and Wickens, C.D. (Eds.) 1993. Workload Transition: Implications for Individual and Team Performance. Washington, DC, National Academy Press.

The NAS Committee on Human Factors addressed the effects of prolonged periods of underload on the performance of tank crews. The committee realized that the key problems were less related to performance during underload itself, than to the consequences of prolonged underload on subsequent performance when workload is suddenly increased, i.e., a workload transition. The focus in this monograph is on team performance relative to workload transitions. For the present purposes of driver role in AHS, we have not focused on the team aspects of the information, but on the relevant topics such as workload, workload transition, stress, vigilance, monitoring, risk, and decision making as they relate to individual performance. The first objective of this study was to review the concept of work underload and asses the state of research knowledge and its effects on subsequent high-workload task performance. Factors thought to affect performance following workload transitions are: (1) circadian rhythm, (2) sleep deprivation, and (3) sleep inertia. Many studies have documented the deleterious effects of both sleep loss and misalignment of circadian phase on performance and safety.

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Accident investigations reveal that poor work-rest scheduling can jeopardize safety in most transportation modes (Graeber, 1988). Vigilance Several factors affect vigilance behavior: psychophysical variables (the modality, conspicuity, and probability of signals, nonsignal event rate, task complexity), environmental variables (noise, vibration), and operator state (sleep loss, task-induced stress). Approaches for enhancing the quality of sustained attention in operational settings include reduction in signal uncertainty, moderation of environmental sources of stress, and training. Vigilance tasks often have been viewed as minimally demanding, tedious situations (Dember and Warm, 1979; Parasuraman, 1984); however, current research is beginning to suggest that vigilance tasks can be quite demanding and induce much stress (p. 36). Expectancy Expectancy is driven by the frequency with which [targets or events] have occurred in the past. For example, nuclear power operators rarely encounter serious failures. A similar situation probably exists for air crews of commercial airliners. This also would be true for the driver in an AHS situation. Expectancy is important because it influences the speed with which people can respond to discrete events (Wickens, 1992). Aircrews normally have little or no advance warning that a crisis is about to occur, and when it does, the crew must respond immediately. However, as in the nuclear power environment, the probability that a transition event [crisis] will occur is quite low. Personal Risk Risk of personal injury or death influences the task. The AHS driver, like the nuclear power operator and the airline pilot, experience little risk until after a transition event occurs. Both risk and environmental factors influence the stress that is experienced and can affect the ability of the operator to respond effectively. Task Settings Similar to AHS In addition to commercial airline pilots and nuclear power operators, the railroad engineer has a task similar to that of an AHS driver given responsibility for monitoring the health of the automatic systems and for detecting obstacles. Note that in all of these cases, except AHS, the operator is selected, highly trained, and is subject to constraints on the work-rest cycle. The nuclear power operator has the same potential problem with vigilance as the rail industry, and the AHS driver. Nuclear power plant operators encounter periods of extreme cognitive underload, especially during evening and late night work shifts, followed potentially be extreme sensory and cognitive overload (in the unlikely event of an emergency). If a nuclear plant is operating properly, virtually no operator intervention is required and the job becomes

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on e of a classic continuous vigilance task, complicated by the fact that the operators know the automatic monitoring systems will probably alert them if something goes wrong. One can imagine the extreme boredom and fatigue that can occur at three o’clock in the morning with everything operating smoothly (p. 38). One difference between the AHS driver’s job and the nuclear power operator’s job is the complexity of the post-transition task. The AHS driver has a very limited number of response options, the most likely of which is to assume manual control. The nuclear power plant operator, and to a large extent, the commercial airline pilot, has a more complex task of diagnosing system faults and activating emergency procedures. This task complexity and the potentially disastrous consequences of operator error are reflected in the lengthy training requirements and costly simulation and training facilities dedicated to both the airline and nuclear power industries. Workload & Task Structure The way in which a task is organized, the rate at which information is presented or error signals change, the length of time the task must be performed, and the levels of speed and accuracy that the operator must try to achieve have a significant impact on the workload imposed during its performance. (p. 58) When performing prolonged tasks, particularly those that are respective and monotonous or present little task-relevant information, people become bored and performance becomes less efficient, e.g., operators make errors, miss relevant signals, respond less frequently or more slowly, or change their decision criteria (Hockey, 1986). (p. 67). Task Input Factors: Information from the Visual Scene Operators often rely on visual cues in the external scene (i.e., optical flow, structural transformations, terrain features) to estimate their position, orientation, speed, and heading. Perceiving spatial relationships and states is difficult if available visual cues are insufficient. At night, visibility is often limited, leading to the use of light sources (headlamps) and visual aids such as night vision goggles and thermal imaging systems. In monitoring or search tasks, attention is directed toward different positions in space, particular information sources, or both. These tasks involve both temporal uncertainty (i.e., signals occur infrequently and at unpredictable times) and spatial uncertainty (i.e., a signal may occur in any one of many spatial locations). Stress The concept of “arousal” is important for understanding the relationship between stress and performance. The relation between arousal and performance has been described as an inverted U-shaped function, known as the Yerkes-Dodson law. ------------------------------------------

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Insert Figure: Yerkes-Dodson here ------------------------------------------Most relevant to the AHS driver role, performance is low when arousal is low. As arousal increases, performance increases, up to some optimum range. Further increases in arousal are associated with high stress, high workload conditions, in which performance again decreases. Circadian Effect The relationship between human performance and the 24-hr cycle of body temperature is well known. Studies of human performance among shift workers demonstrate strikingly similar results. During the latter half of the night, responses to calls for service take the longest; performance on an F-104 flight simulator is worst; shooting range performance among military personnel is least efficient; mental arithmetic is slowest; alertness is lowest; shortterm memory is markedly impaired; and the rate of single-vehicle truck accidents due to sleepiness is the greatest (p. 125). It is clear that the capability of the AHS driver to detect potential obstacles is likely to be very poor during the early morning hours. VIGILANCE & TARGET DETECTION [This section of the NAS Workload Transition Committee report was written by Dr. J.S. Warm]. “The task of monitoring for infrequent signals is one for which humans are not well suited…” (p 139). Vigilance or sustained attention refers to the ability of observers to maintain their focus of attention and to remain alert to stimuli for prolonged periods of time. Early studies of the vigilance effect by Macworth (1948) found that test subjects became progressively more inefficient at detecting signals as the watch continued, and that this inefficiency did not take long to develop. The accuracy of signal detection declined about 10% after only 30 minutes on watch, then showed a more gradual decline over the remaining two-hour session. This decline in performance over time has been confirmed in a large number of subsequent investigations and has been labeled the “vigilance decrement.” The Implications of Automation for Vigilance The use of automatic control and computing systems for the acquisition, storage, and processing of information has altered the role of the human operator from that of active

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controller to a more executive function, a development that Sheridan (1972) has characterized as a shift from active to supervisory control. (p. 141) Vigilance is a crucial aspect of the reliability of human performance in a wide variety of activities including industrial quality control, air traffic control, nuclear power plant operations, and long distance driving (Warm, 1984). A summary of the conditions that influence vigilance performance has been suggested by Jerison and modified by Warm and Berch (1985), which takes the following form: P = f ( M, S, U, B, C ) where performance (P) is a function of the sensory modality of the signals (M), the salience of signals (S), stimulus uncertainty (U), the characteristics of the background of nonsignal events (B), and task complexity (C). (p. 142) Signal Modality (M) Vigilance performance tends to be better with auditory signals than with visual or cutaneous signals. Performance with redundant, multi-modal signals is better than for any one sensory modality. The implications of these findings are not good for the potential role of obstacle detection by the AHS driver. Signal Salience (S) The probability of target detection is positively related to the amplitude and duration of the signal. Signals of brief duration are more likely to be missed. The implications for AHS are not straightforward, because the nature of the “signal” of potential obstacles is difficult to characterize in advance, short of listing all possible targets that could be considered obstacles, and defining the population density of their eccentricity, size (visual angle), and contrast. Stimulus Uncertainty (U) AHS drivers engaged in monitoring for obstacle detection will be faced with both temporal and spatial uncertainty. High temporal uncertainly leads to poor signal detection. Likewise, high spatial uncertainty is associated with poor signal detection performance. Unless the driver has reason to believe that obstacles are likely in a given area or a during a given period of time, a high degree of temporal and spatial uncertainly will exist and detection performance can be expected to be poor. Background (B) Non-signal events often are presented in the context of vigilance experiments. Thus critical signals for detection are embedded within a matrix of recurrent background events. The frequency of background events affects the signal detection rate. It is not surprising that the context of the task-- sorting for critical signals among non-critical signals -- affects task

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performance. With respect to the AHS question, the characteristics of background, noncritical, signals is not amenable to precise definition in an obstacle detection task. Any object that is detected but classified as a non-obstacle would be a member of the set of background events. Therefore the frequency of background events, and their characteristics, would vary widely depending on the highway environment. Stimulus Complexity (C) The signal used in vigilance experiments is usually simple, such as discrete changes in the intensity, duration, or movement of an object on a single display. In operational settings, more complex discriminations will be involved and observers may be required to cope with multiple signal sources. Investigations of the consequences of increased task complexity have led to mixed results. When the cognitive demand is increased, the vigilance decrement tends to be reduced. But other studies have reported that the beneficial effects of increased cognitive demand are limited.

Signal Detection and Vigilance Detection of a signal depends on nonperceptual factors that include the person’s detection goals, expectations about the nature of the signal, and the consequences of correct and incorrect responses. These factors comprise the individual’s response criterion, or willingness to emit a detection response. Signal detection theory (Green & Swets, 1974) deals with these issues and defines d’ as a measure of perceptual sensitivity and B as a criterion for responding. A number of investigations have indicated that the vigilance decrement reflects a shift to a more conservative response criterion (shift in B). We suggest that the concept of a response criterion (B) is important for the AHS driver, particularly in a platooning situation. The driver of a lead car in a platoon may be likely to adopt a very conservative criterion because any false positive (the driver incorrectly thinks he/she detects an obstacle) could have potent consequences. If the driver initiates evasive action unnecessarily, the occupants (drivers and passengers) in nearby vehicles, both in the platoon, in following platoons, and in adjacent lanes will be affected, most likely in a negative manner. The lead driver may be very reluctant to create an angry or hostile attitude in drivers who may be following at very close quarters (circa 1 meter) for an extended time. This would be a practical example of a criterion (B) shift. Task-Induced Stress in Vigilance Evidence is accumulating to indicate that vigilance tasks are not benign, but can be quire demanding and induce considerable stress in participants. Physiological and psychophysiological measures of stress indicated that vigilance tasks as well as high-workload tasks led to increased levels of effort and stress.

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In the AHS application, assigning the role of obstacle detector to the driver is likely to lead to stress for many drivers. The stress will be amplified if the lead driver in a platoon is given responsibility for the safety of the members of the platoon through vigilant detection of potential obstacles. Self reports of mood after participating in vigilance tasks show that participants feel more sleepy and fatigued after the session than before. Other reports of negative mood changes have included increased drowsiness, strain, and fatigue. Similarly, subjective reports of workload associated with vigilance tasks indicate high workload ratings, based on the NASA Task Load Index (TLX) technique. Factors that contributed to overall workload were mental demand and frustration, a result that provides further indication of the stress of sustained attention. RECOMMENDATIONS ADAPTED FROM THE WORKLOAD TRANSITION REPORT Signal enhancement increases signal detection in any task. Engineering advances that can identify potential obstacles provide the potential for alerting the driver. Since auditory signals are less spatially constrained and more alerting, an auditory alert is recommended. The time to respond clearly is important for the AHS obstacle avoidance scenario.

Sheridan, T.B. (1987). Supervisory Control. In Salvendy (Ed.) Handbook of Human Factors. New York: John Wiley & Sons. ( pp. 1243- 1268)

A definition of supervisory control is given as a figure (see Figure 9.6.1), describing stages of automation ranging from manual control to fully automatic control. -----------------------------------------Fig 9.6.1 goes here – p. 1245 -----------------------------------------------------

Supervisory control indicates that one or more human operators are setting initial conditions for, intermittently adjusting, and receiving information from a computer that itself closes a control loop (i.e. interconnects) through external sensors, effectors, and the task environment. The hierarchical nature of supervisory control is shown in Figure 9.6.2, p. 1247.

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------------------------------------Figure 9.6.2 goes here -------------------------------------Aircraft autopilots are now layered, meaning the pilot can select among various forms and levels. At the lowest level, he or she can set a new heading or rate of climb. Or, the pilot can program a sequence of heading changes at various way-points, or a sequence of climb rates initiated at various altitudes. Or, the pilot can program a flight management system to take the aircraft to touchdown on a particular runway at a distant city. The human supervisor’s roles are: (1) planning off-line what task to do and how to do it; (2) teaching (or programming) the computer what was planned; (3) monitoring the automatic action on-line to make sure all is going as planned and to detect failures; (4) intervening, to take over control after the desired goal state has been reached satisfactorily, or interrupting the automatic control in emergencies to specify a new goal state… The human supervisor monitors the automated execution of the task to ensure proper control/ this includes intermittent adjustment or trimming, if the process performance remains within satisfactory limits, to detect if and when it goes outside limits, and to diagnose failures or other abnormalities. The supervisory controller tends to be removed from full and immediate knowledge about the controlled process. One of the factors affecting the supervisor’s decision to intervene (and his/her success in doing so) is mental workload. When a supervisory control system is operating well in the automatic mode, the supervisor may have little concern. When there is a failure and sudden intervention is required, the mental workload may be considerably higher than in direct manual control , where in the later case the operator is already actively participating in the control loop. In the former case the supervisor may have to undergo a sudden change from initial inattention to acquire information and learn what is going on , then make a decision about how to cope. Quite likely this will be a rapid transient from low to high mental workload. [This “workload transition” is the subject of the Huey and Wickens (1993) monograph from the National Academy of Sciences]. The simplest model of supervisory control might be that of nested control loops (see figure 9.6.10, page 1258) where one or more inner loops are automatic and the outer lone is manual.. In driving a car, the functions of navigation, guidance, and control are all done by a person, and can be seen to correspond roughly to knowledge-based, skill-based, and rule-based behaviors in Rasmussen’s terms. In AHS, the control and guidance functions are automated and the navigation function would be allocated to the driver.

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Sheridan’s graph of “Degrees of Automation” (Table 9.6.1, page 1264) may be relevant to transitional states of control in AHS, into or out of an automated lane or state. It also is relevant to the role assigned to the driver when cruising under automated control

Kantowitz, B.H. and Sorkin, R.D. (1987). Allocation of Functions. In Salvendy (Ed.) Handbook of Human Factors. New York: John Wiley & Sons. (pp. 355-369). The division of work between people and machines is called function allocation. Allocation of functions determines not only how well a person-machine system will operate but also the quality of working life for the personnel who must toil within that system. The authors discuss Fitt’s List as an early attempt at allocating functions between humans and machines according to their relative strengths and capabilities, i.e. according to tables of relative merit. But this approach is not entirely satisfactory because, as Jordan (1963) pointed out: People are flexible but inconsistent, whereas machines are consistent but inflexible. The allocation of functions between people and machines must go beyond the clear engineering rules we would follow when allocating functions between two machine subsystems. One design approach is to “let the machine do it;” then assign any left-over functions to the human. Allocating the task of obstacle detection to the driver in AHS seems to fit this category. The leftover functions in this type of allocation may not necessarily leave the human subsystem with a reasonable set of tasks to perform. Unreasonable sets of tasks arise in two main ways. First, the load created by the tasks may not match human capabilities. Underload can be equally fatal to the human as overload. Stress is created when task demands do not match human capabilities. Function allocation whereby the human is left with whatever the machine can’t do often is accompanied by a design philosophy that regards the human as a nuisance. Such designers would prefer not to have humans in the system at all. An example is an allocation policy that gives the human operator responsibility for only the abnormal system conditions. When the system is operating properly, the designer has intended that the human keep hands off. This type of approach makes human incompetence a self-fulfilling prophecy. If the human has not been allocated functions when the system is operating properly, it is most unlikely that he or she can operate effectively in any manual backup mode. Perils of Automation: (1) Newly automated systems seldom provide all of the anticipated benefits. For example, the first version of the ground proximity warning system produced many false alarms. This could result in decreased safety and increased human workload. (2) Failure of automated equipment leads o problems of credibility. If users have an option, they will not rely on equipment they do not trust .

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(3) Training requirements are often increased by automation. The user must b able to operate in both manual and automated modes and manage the transition between them. (4) Designers often fail to anticipate new problems that the automated systems will create because they have focused only on the potential benefits. For example, incidents have occurred when pilots entered incorrect way-point coordinates in inertial navigation systems. Indeed, this has been suggested as a likely cause for the demise of a Korean airliner that wandered into Russian airspace and was shot down. AHS must be particularly impervious to erroneous human input because the users will not be as highly selected and trained as airline pilots, for example. (5) Automation makes pilots into system monitors rather than active controllers. This change in role is likely to make them less mentally prepared to take control suddenly from the automatic system in an emergency. Also, unwitting reliance on automated systems can induce failure to notice gradual development of error in an automated system, as illustrated by the controlled flight into terrain of Eastern flight 401 near Miami airport in 1980 killing 99 people. Dynamic allocation allows changes in the function allocation between the user and the system as needed. The user may choose the settings or, conceivably, the system could automatically shift allocation as a function of operational demands or the state of the user (biocybernetics).

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REFERENCES

Boff, K.R. and Lincoln, J.E. (1988). Engineering Data Compendium: Human Perception and Performance. Wright-Patterson AFB, OH: Army Aerospace Medical Research Laboratory. Brown, I.D. (1979). Can Ergonomics Improve Primary Safety in Road Transport Systems? Ergonomics, 22(2), 109-116. Chambers, A.B. and Nagel, D.C. (1985). Pilots of the Future: Human or Computer? Communications of the Association of Computing Machinery, 28, 1187-1199. Davies, D.R. and Tune, G.S. (1969). Human Vigilance Performance. New York: Elsevier. Davies, D.R. and Parasuraman, R. (1982). The Psychology of Vigilance. Yew York: Academic Press. Dember, W.N. and Warm, J.S. (1979). Psychology of Perception (2nd Edition). New York: Holt, Rinehart & Winston. Graeber, R.C. (1988). Aircrew fatigue and circadian rhythmicity. In E. Wiener and D. Nagel, (Eds.) Human Factors in Aviation. San Diego, CA: Academic Press. Green, D.M. and Swets, J. Signal Detection Theory and Psychophysics. New York: John Wiley. Hockey, G.R.J. (1986). Changes in operator efficiency as a function of environmental stress, fatigue, and circadian rhythms. Chapter 44. In Boff, K.R., Kaufman, L. and Thomas, J.P. (Eds.) Handbook of Perception and Performance, Vol.II, Cognitive Processes and Performance. New York: John Wiley. Haber, R.N. and Hershenson, M. (1980), The Psychology of Visual Perception. New York: Holt, Rinehart and Winston. Jordan, N. (1963). Allocation of functions between man and machines in automated systems. Journal of Applied Psychology, 47, 161-165. Kantowitz, B.H. and Sorkin, R.D. Allocation of Functions. In Salvendy (Ed.) Handbook of Human Factors. New York: John Wiley & Sons, Chapter 3.3, pages 355-369. Kahneman, D. (1973). Attention and Effort. New York: Prentice Hall. Keele, S.W. (1973). Attention and Human Performance. Pacific Palisades, CA: Goodyear.

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Mackworth, N.H. (1948). The breakdown of vigilance during prolonged visual search. Quarterly Journal of Experimental Psychology. 1, 66-21. Parasuraman, R. (1984). The psychobiology of sustained attention. In J.S. Warm (Ed.), Sustained Attention in Human Performance. Chichester, UK: Wiley. Parasuraman, R. (1986). Vigilance, Monitoring, and Search. Chapter 43. In Boff, K.R., Kaufman, L. and Thomas, J.P. (Eds.) Handbook of Perception and Performance, Vol.II, Cognitive Processes and Performance. New York: John Wiley. Parasuraman, R. (1987) Human-Computer Monitoring. Human Factors, 29(6), 695-706. Parasuraman, R. and Mouloua, M. (1996). Automation and Human Performance: Theory and Applications. Mahwah, NJ: Lawrence Earlbaum. Schoonard, J.W., Gould, J.D., and Miller, L.A. (1973). Studies of visual inspection. Ergonomics, 16, 365-379. Sheridan, T. (1972). On how often the supervisor should sample. IEEE Transactions on Systems, Sciences, and Cybernetics. SSC-6, 140-145. Sheridan, T.B. (1987). Supervisory Control. In G. Salvendy (Ed.) Handbook of Human Factors. (pp. 1243- 1268). New York: John Wiley & Sons. Swets, J.A. (1977). Signal detection theory applied to vigilance. In R.R. Mackie (Ed.), Vigilance Theory Operational Performance and Physiological Correlates (pp. 705718). New York: Plenum Press. Warm, J.S. (Ed.) (1984). Sustained Attention in Human Performance. New York: John Wiley & Sons. Warm, J.S. (1993). Vigilance and target detection. In B.M. Huey and C.D. Wickens (Eds.), Workload Transition: Implications for Individual and Team Performance. (pp. 139170), Washington, DC: National Academy Press. Warm, J.S. and Berch, D.B. Sustained attention in the mentally retarded: The vigilance paradigm. In N.R. Ellis and N.W. Bray, (Eds.), International Review of Research in Mental Retardation (Vol 13; pp. 1-41). Orlando, FL: Academic Press. Wickens, C.D. (1992). Engineering Psychology and Human Performance. New York: HarperCollins.

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OTHER OBSERVATIONS OR TOPICS TO BE DISCUSSED

PAYOFF MATRICES It is important that the consequences (payoff matrix) of the 2x2 decision matrix be examined with respect to the obstacle avoidance driver role: Correct Detection -- choose evasive maneuver Missed Signal -- probable collision Correct Rejection -- continue travel False Alarm -- inappropriate evasive maneuver (implications on platoon & nearby traffic unknown) Likewise, the response repertoire available to the driver upon correct detection of an obstacle must be defined. [take control & make evasive maneuver, activate autoevade, communicate with platoon, call for help… And, the response time distribution for this series of decisions needs to be estimated. ]

ANGULAR POSITION OF A TARGET The importance of angular position of the target suggests that an analysis of the AHS driver role of monitoring for obstacle detection should include a description of the probability density function of potential targets. This exercise in geometry and dynamics is necessary to help define, for expected AHS speeds, the maximum off-angle of potential obstacle classes. When is a deer an obstacle? A deer is running toward the AHS lane. If the driver detects it, does the driver consider it to be an obstacle if own-vehicle’s velocity is sufficient to ensure that the deer will arrive too “late” to cause a collision? Should this information be communicated to the AHS for the benefit of subsequent vehicles in the same lane? DRIVER RESPONSE TIME IS CRITICAL The time for a driver to respond will be dependent on factors such as the driver’s current state of alertness. Minimal response times to an alert are likely to be on the order of ½ to 1 sec; median response times are likely to be on the order of several seconds; worst case may be 10 seconds up to complete failure to respond. The distance traveled over these periods should be considered for estimates of effective obstacle avoidance response envelopes of AHS drivers.

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APPLICABILITY OF VIGILANCE RESEARCH TO AHS Most of the research on human monitoring performance has focused on human operators detecting a visual signal on an electronic display or a set of displays. The relevance of these research findings for the AHS driver role of obstacle detection is assumed, rather than explicit. The core processes of human detection performance as a function of time, signal strength, spatial and temporal uncertainty are assumed to be the same, whether the observer is detecting a signal from displays or through the windshield, in the three-dimensional world.

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VIGILANCE RESEARCH AND HIGHWAY VEHICLE AUTOMATION James C. Miller, Ph.D. [This material will be integrated into the main text in the final document]

A primary role of the Human Factors Engineer in system design is to assist other design engineers to (1) exploit human strengths that contribute to safe and successful operation of the system, and (2) protect the system from potentially harmful human weaknesses. The human operator is the most variable and least predictable component in any semi-automated system. If a human operator is needed in a system, the state and inputs of the human operator must be examined carefully and quantitatively with respect to system design. This examination must occur early in the system design process. The later it occurs, the more costly are the potential errors (e.g., Chapanis, 1965). These costs may be financial, in terms of redesign, redevelopment and re-test; or the costs may be more catastrophic, in terms of human injury and death. Within this context, the selective allocation of system functions to the human operator and to automation has received quite a bit of attention in aviation research (e.g., Wiener and Curry, 1980; Wickens and Kessel, 1979). Much of that information is applicable to the topic of automated driving. The most common philosophy applied to automating a control system is to automate repetitive, manually controlled processes, increasing the freedom of the human operator to engage in planning, goal selection, pattern recognition, fault detection, and other cognitive, supervisory functions. This rationale is reasonable because manual control, particularly over extended time periods, is not a particular strength of the human operator. Given adequate feedback, an automated system can perform tedious and complex tracking tasks far better than a human. On the other hand, this automation philosophy often forces the human into the role of a system monitor, a function handled poorly in most cases by the human brain (e.g., Mackie, 1977; Miller and Mackie, 1980). GENERAL HUMAN FACTORS CONCERNS Hancock and Parasuraman (1992) described six areas of human factors concern in vehicle automation design. (1) “What is the tradeoff between high workload and high fatigue on the one hand versus boredom and complacency on the other?” (We note that the demand to monitor an automated system may, itself, contribute to workload.) (2) “The tradeoff between replacing intelligent human capabilities with ‘dumb’ devices...will need to be re-examined...” They cite D. Norman (1991) to support the idea that automation should be dumber or smarter than at present, because current devices do not keep the operator sufficiently informed. (3) Some human factors problems introduced by automation will not be discernible immediately.

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(4) For collision-avoidance devices, what are the tradeoffs between the effects of false positive and false negative detections on the human operator’s performance? (5) Will this automation effort be subjected to the common thought pattern that calls for the “mere addition of more automation as problems are sequentially encountered?” (6) There are concerns about the designs of controls and displays and about dealing with drivers of widely varying abilities. All of these concerns impinge on the issue of driver vigilance in an automated vehicle. The first issue suggests that driver-out-of-the-loop problems and some sort of vigilance decrement may also be associated with automated vehicle operations. The second suggests that uniquely elegant human pattern recognition systems may be replaced by less-competent sensors. When the signal-to-noise ratio (in a sensible frequency band) is low in a vigilance paradigm, human pattern recognition systems are far more effective than sensors for the perceptual aspects of signal detection. The un-tutored system operator may assume the opposite. The third issue suggests that subtle vigilance problems may not be recognizable until after the occurrence of many accidents involving automated vehicles. The fourth issue raises the classic vigilance problem characterized in the story of the boy who cried, “Wolf!” A high false alarm rate will cause the operator to ignore indications of system malfunction, and detection rates will drop to near 0%. The fifth issue suggests that, when automation problems are caused by vigilance failures on the part of the operator, they may be misinterpreted and the wrong “fixes” implemented. Finally, the last issue suggests that displays may not support optimal performance by the human monitor, nor allow for interindividual variations in the ability to remain vigilant. Hancock, Parasuraman and Byrne (199x) considered driver-centered issues in automation. They had several relevant observations concerning questions related to keeping the driver in the loop in the design of automated collision-avoidance systems. (1) They noted that automated systems may not adequately perform the functions of detection and evaluation, and they recommended keeping the human in the loop until a time when adequate computational power may be brought to bear on that problem. (2) For general acceptance, they noted that “any surrogate automated controller must exceed human response capability under all operational driving conditions...” (3) They discussed the idea that the human should be the “backup system of last resort.” They noted that “lessons from aviation, medical systems, process control and similar domains have taught the fallacies of such a strategy.” Finally, they brought up the idea of adaptive systems “that emphasize the use of mutually adaptive capability on behalf of both human and machine to promote flexible and rapid response to uncertain conditions and unusual task demands.” Integrating these ideas, one may see a picture of a system design that exploits the strengths of the human operator while protecting the system from the weaknesses of the operator. One strength that may be exploited is visual pattern recognition. One weakness that must be dealt with is the general inability of the human to sustain attention in low workload conditions. Tsang and Vidulich (1989) described four human factors concerns associated in aircraft cockpits with “automating the inner loop’s activities (repetitive manual processes), freeing the human for [the] outer loop’s activities (planning and goal selection).”. The concerns included

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the (1) “increased monitoring load placed on the pilot,” (2) a “high level of responsibility with ‘little’ to do,” (3) the “out-of-the-loop” problem, and (4) “loss of manual skill proficiency.” Certainly, the first three items are human factors concerns that must be dealt with in similar applications of automation to the operation of highway vehicles. Citing the extensive review on monitoring behavior and supervisory control by Moray (1986), Tsang and Vidulich noted that monitoring (scanning one or more displays for information) is a necessary part of supervisory control (e.g., failure detection and fault diagnosis, decision making). Monitoring strategy is influenced by practice, endogenous uncertainty due to forgetting, the needed accuracy of observations, intercorrelations among sources of information, the cost of making an observation, and the cost of missing a signal. Practice was the most important factor influencing monitoring performance, with the development of an underlying model of the optimal monitoring process being the result of practice. These observations indicate that, as highway vehicle automation hardware systems are developed and fielded, parallel efforts must be made to develop training systems and curricula that allow drivers to learn optimal monitoring strategies. Additionally, displays must be designed such that they provide required accuracy and are easily sensed, perceived and interpreted. DRIVER VIGILANCE In his comprehensive book about truck driver fatigue, McDonald (1984) considered the vigilance aspects of truck driving. He emphasized the important papers by Hildebrandt et al. (1974, 1975). They examined an engineer alerting device called SIFA which required a button or lever response to a visual signal and operated on a 30-sec interstimulus interval. Lack of response after 2.5 sec turned on an alerting buzzer (“hooter”). Lack of response after another 2.5 sec activated the train’s brakes. Hildebrandt et al. demonstrated the important effects of time of day and the length of the rest period between duty periods on the engineers’ ability to respond to the alerting device. McDonald concluded from this, and the results of other relevant investigations, that one may predict that a driver will miss certain kinds of signals after prolonged driving and at specific times of day, but that “we can have only a vague notion of the sort of signals that are likely to be missed, and we have virtually no insight into the process of deterioration” (pp.110-111). We may also conclude that substantial amounts of research may be needed to determine what kinds of signals should be used in automated systems to acquire the driver’s attention. McDonald went on to review studies suggesting that deteriorating oculomotor function, leading to deterioration in observation patterns, may contribute to the breakdown of the driver’s attentional system. I.D. Brown’s view of vigilance performance deterioration, in the context of driver fatigue, focused on an increase in attention selectivity associated with time of day and time on task (Brown, 1994). Brown suggested that visual search behavior adapts to perceived changes in perceptual ability and that this adaptation is a self-regulating process. He also suggested that one cannot predict the occurrence of the breakdown of this self-regulation on the basis of the length of the drive. However, he suggested that one can predict that the breakdown is likely at

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night, especially around 04:00, and when task and environmental demands are low. Brown noted that, under these latter conditions, Michon and Wertheim (1978) found that “a driver’s visual sampling behavior may cease to be dictated by road and traffic demands and instead may be determined increasingly by internal oculomotor programs based on the experienced predictability of road and traffic events. This is clearly a dangerous state of affairs.” (Brown, 1994, pg. 307). The validity of Brown’s insights into driver vigilance deterioration are supported by his 25 years of research in this area. The implications of his interpretation of the driver-vigilance research literature include (1) avoiding vehicle automation designs that place the driver in a low task demand environment, and (2) avoiding dependence upon the human monitor during the pre-dawn hours. It should be noted that this pre-dawn proscription from reliance upon the human monitor should probably be applied also to the mid-afternoon period. Mitler and Miller (1996) developed a quantitative predictor of error probability as a function of time of day. The inputs to the predictor equation included very large samples of highway accidents and falling asleep at the wheel. The predictor showed a primary peak in the pre-dawn hours and a secondary peak in the mid-afternoon hours. These peaks were attributed to circadian and circasemidian cycles in the central nervous system, respectively. The same data set described by Hildebrandt et al. (1974, 1975) spawned an analysis by Fruhstorfer et al. (1977) concerning the train drivers’ ability to repeatedly reset the alerting device while displaying the standardized, electroencephalographic (EEG) signs of Stage 1 sleep. They hypothesized that the engineer would spontaneously reset the device at intervals shorter than 30 sec, using a stable rhythm. Consequently, this habitual reset would improve the ability of the engineer to operate the device correctly, "even in states of lowered vigilance when his ability to perform the primary task may have greatly suffered." They looked at the rhythmic patterns of responses in operational locomotive cabs (4 months, 42,000 km, 200 engineers) and found stable response cycles of 4 to 20 per minute, supporting their hypothesis. They also showed these stable, rhythmic response patterns in the laboratory, where psychophysiological measures of brain and eye activity showed Stage 1 sleep patterns, again supporting their hypothesis. They concluded that the engineers learned to use the device with minimal effort. They developed "a spontaneous, self-paced and rhythmic way of handling the system." This allowed "correct operation of the system even at levels of vigilance when the main task [safely operating the train] probably cannot be performed without mistakes." The lesson of the findings by Frushtorfer et al. is that a simplistic alerting device cannot be relied upon to make sure that the system operator is (1) awake and (2) attending to system and environmental cues. At the very least, an alerting device must (1) operate on a random schedule with wide variance in the interstimulus interval and (2) contain a cognitive component within the required response.

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Wiener (1987) quoted a newspaper article about the Automatic Train Operation (ATO) device used by the Dade County Metrorail system. The account provides an operational perspective for the research conducted by Parasuraman and his colleagues concerning the effects of automation on human performance. The newspaper article explained how the ATO device was designed to bring the train to a stop at each station in exactly the right place with respect to the station platform. However, one train driver noted that the ATO failed to act at all at about every tenth stop. Thus, the driver was forced to stop the train manually, then position it along the platform correctly. Wiener’s lesson from this observation was that although one may automate, the human operator will often be needed. The observation also shows us that human operators must deal with unreliable automated systems. Research by Parasuraman and colleagues indicates that automation reliability is a primary factor affecting human operator vigilance in automated systems. Perhaps the most important contribution in the area of automation and human vigilance performance by Parasuraman and colleagues was presented by Parasuraman, Molloy and Singh (1993). They noted that the coding manual of the NASA Aviation Safety Reporting System (maintained primarily for the FAA) defined complacency as “self-satisfaction which may result in non-vigilance based on an unjustified assumption of satisfactory system state.” In Parasuraman et al. (1993), complacency was defined as a failure to respond to an automation malfunction. The methods and results were summarized by Singh et al. (199x): “Subjects performed three flight-related tasks--tracking, fuel management, and system monitoring--for several sessions. The last task, system monitoring, was controlled by an automation routine that was not perfectly reliable. Parasurman et al. reasoned that in a multitask environment, automation that is consistently reliable (but less than 100% reliable) is more likely to induce a condition or complacency than is automation that varies. This hypothesis was tested by comparing the “back-up” monitoring performance of two groups of subjects: a constant-reliability group, for which the reliability of the automated monitoring task (percentage of “system malfunctions” detected) was constant over time; and a variable-reliability group, for which automation reliability alternated between low and high levels every 10 min. The results strongly supported the prediction. Subjects detected over 72% of system malfunctions during training under manual control. Nevertheless, after approximately 20 min under automation control, the detection rate of automation failure was markedly poorer in the constant-reliability condition (mean = 32.7%) than in the variable-reliability condition (mean = 81.6%). This effect was obtained only under multitask conditions in which subjects had responsibility for more than the function under automation control. When subjects simply had to “back up” the automation routine controlling the system monitoring task without any other duties, variation in automation reliability had no effect on detection of automation failures, and monitoring was efficient.

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The results indicated that certain automation characteristics (e.g., reliability and consistency) can lead to poor monitoring.” (page 358) The results could not be attributed to experimenter effects, initial performance levels, speedaccuracy trade-offs, signal rate, or visual scanning behavior. There were a number of salient points elucidated by Parasuraman et al. (1993) in their discussion of their results. (1) The mean detection rates concealed “the fact that, in the constant-reliability condition, some subjects simply failed to detect any automation failures in a given 10-min block; six subjects had at least one instance of 0% detection in a 10-min block, whereas no such instances occurred for subjects in the variable-reliability condition” They cited this observation as the first empirical evidence of complacency demonstrated in a controlled experiment. (2) “Surprisingly, although the effect of complacency on performance did increase with time, the effect was present in the very first session. ...automation-induced complacency had performance consequences after only about 20 min spent under automation control.” (3) “...the dominant factor influencing complacency seemed to be the consistency of performance of the automation.” (4) “....automation-induced complacency is more easily detectable in a multi-task environment when operators are responsible for many functions.” This implied that “complacency is not necessarily associated with low workload.” (5) They examined the construct of complacency, itself. Since complacent performance may have many causes, such as believing that an action has already been accomplished, complacency cannot be uniquely associated with other constructs such as boredom, vigilance or workload. Using their results as support, Parasuraman et al. (1993) argued that operator complacency might be reduced by using “adaptive function allocation...to transfer manual control of an automated task temporarily to the operator during noncritical work periods.” They also cited other experimental support for this idea. Subsequently, Parasuraman and colleagues examined further the effects of adaptive task allocation (Parasuraman et al., 1996). The subjects monitored an automated engine-status display, tracked manually and managed fuel manually. The engine status automation adaptivity was based upon a “model” schedule for one group of subjects and based upon an individual’s monitoring performance. For both groups, the engine monitoring task became manual for a period during the middle of the testing session. However, this was true in the latter group only if detections fell below 55% of engine failures for the previous 40 minutes. There were also non-adaptive controls. All groups had low detection probabilities, around 30%, during the first part of the session. The model-based group’s detection rate reached nearly 80% during the manual control period. Both adaptive groups’ detection probabilities increased significantly, to above 50%, after the manual control portion of the session. Parasuraman et al. cited these results as support for the idea that a transfer of control to the human operator during low-workload periods may increase signal detection rates. Important factors in the design of adaptive system include “the adaptive algorithm, the frequency of adaptive changes, automation reliability and consistency, the type of interface, and contextual factors...” (op. cit.).

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A parallel investigation (Molloy et al., 1996) addressed the question of whether task complexity and time on task had effects on monitoring performance when only a single failure occurred over the course of a 30-min test session. The same multi-task battery was used as described for Parasuraman et al. (1996). Monitoring performance was poorer under automation conditions than under manual conditions, slower under the most complex task demands and the most simple than under moderately complex task demands, and poorer later in a session than earlier in a session. Five papers from the 1976 NATO Symposium on vigilance, held 3-6 August in St. Vincent, Italy (Mackie, 1977), dealt to one degree or another with the relationships between vigilance and driving (Riemersma et al., 19977; Caille and Bassano, 1977; Fagerstrom and Lisper, 1977; O’Hanlon and Kelley, 1977; Mackie and O’Hanlon, 1977). The first four papers involved long-term night driving, with its natural confounds of circadian rhythm effects and sleep disruption effects. None of the five used vigilance-type measures such as proportions of detections, false alarms or response speeds. Instead, they focused on steering control and physiological measures. The results of these investigation bear on questions concerning the interactions of fatigued drivers with automated systems, but not on specific questions about vigilance and automation. SUPERVISORY CONTROL The human cognitive, supervisory functions of interest are those characterized as knowledgebased, as opposed to those functions that are skill-based or rule-based (Rasmussen, 1978). Knowledge-based functions include the recognition of, and creation of responses to, novel and unpredicted situations for which clearly applicable rules do not exist. This function is, perhaps, the greatest strength that a human operator can add to a system. Among the supervisory functions allocated to the human operator, perhaps the most critical is the combination of failure detection and fault diagnosis (Moray, 1986). The failures of immediate concern in an automated vehicle on the highway fall within the dimensions of absolute velocity, relative velocity, absolute lateral lane position, and relative longitudinal lane position. Whether the cause of a failure is internal or external to the vehicledriver combination, it will affect the vehicle in one of these dimensions. Research is needed to determine whether or not the driver can detect, remain vigilant for, and diagnose problems that arise within these dimensions. By absolute velocity, we refer to the dimension of the absolute speed of the vehicle, independent of links to other vehicles but dependent upon a commanded speed. By relative velocity, we refer to the dimension of the closure rate on a lead vehicle as a platoon is formed. By absolute lateral lane position, we refer to the dimension of the nearness of the vehicle to the lane line or to the center of the lane, independent of links to other vehicles but dependent upon a commanded position. By relative longitudinal lane position, we refer to the dimension of the variation in commanded position with respect to a lead vehicle. Studies of the accordion effect in traffic flow, of rear-end accidents, and of formation flight in military aircraft suggest that human operators need training to attain accuracy in estimating

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vehicle longitudinal closure rates. Studies of driver fatigue suggest that human operators attend to absolute speed and lateral lane position quite carefully when not distracted or fatigued (e.g., Mackie and Miller, 1978). Control groups for studies of the effects of alcohol and other substances suggest acceptable headway limits and variabilities during car following under manual control (e.g., Wetherell, 1986). Beyond these data, the literature supplies little specific information that may be brought to bear on the design of a human operator's perceptual contribution to the monitoring of an automated highway vehicle. Research concerning driver behavior has focused on the perceptual aspects of driver behavior. However, aviation research that deals with analytical behaviors has some possible applicability to human operators aiding automated vehicles. The human operator in an automated vehicle will, in most cases, be forced to diagnose system failures under the pressure of time stress. This is due, simply, to the absolute speed at which automated vehicles operate and the speed with which they move relative to each other. Instead of forming hypotheses in a relatively leisurely manner (Wickens and Flach, 1988), the operators will probably use a pattern-matching approach to failure diagnosis (Ebbeson and Koneci, 1981). EXERCISE AND VIGILANCE PERFORMANCE Poulton (1977) considered the influences of both body temperature and exercise on vigilance performance. Exercise effects were accorded three paragraphs. The last paragraph of the three cited CP Davey’s experiment using the bicycle ergometer (Ergonomics, 1973, 16, 595599, experiment 2). From that experiment stemmed the observation that relatively low levels of exercise (re an individual’s aerobic capacity) probably enhance vigilance performance while relatively high levels lead to degraded performance. Exercise was one of the experimental factors in the Mackie and Miller (1978) DOT study of truck driver performance. The results of that investigation suggested that moderate, boxlifting physical labor (30% of individual aerobic capacity) had a brief (about 30 minutes), positive effect on driver performance.

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APPENDIX A REFERENCES Brown ID (1994). Driver fatigue. Human Factors, 36, 2, 298-314. Caille EJ, Bassano JL (1977). Validation of a behavior analysis methodology: variation of vigilance in night driving as a function of the rate of carboxyhemoglobin. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 59-72. Chapanis A (1965). Man-Machine Engineering. Brooks/Cole Publishing, Wadsworth, London. Ebbeson ED and Koneci V (1981). On external validity in decision making research. In T Wallsten (ed.), Cognitive Processes in Choice and Decisionmaking, Erlbaum, Hillsdale NJ. Fagerström KO, Lisper H-O (1977) Effects of listening to car radio, experience, and personality of the driver on subsidiary reaction time and heart rate in a long-term driving task. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 73-86. Fruhstorfer H, Langanke P, Meinzer K, Peter JH, Pfaff U (1977). Neurophysiological vigilance indicators and operational analysis of a train vigilance monitoring device: a laboratory and field study. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press. Hancock PA, Parasuraman R (1992). Human factors and safety in the design of intelligent vehicle-highway systems (IVHS). J. Safety Res., 23, 181-198. Hancock PA, Parasuraman R, Byrne EA (199x). Driver-centered issues in advanced automation for motor vehicles. In XXX Hildebrandt G, Rohmert W, Rutenfranz J (1974). 12 and 24 hr. rhythms in error frequency of locomotive drivers and the influence of tiredness. International J. Chronobiology, 2, 175-180. Hildebrandt G, Rohmert W, Rutenfranz J (1975). The influence of fatigue and rest period on the circadian variation of error frequency in shift workers (engine drivers). In WP Colquhoun, S Folkard, P Knauth, J Rutenfranz (ed.), Experimental Studies of Shiftwork, Opladen, Westdeutscher Verlag. Mackie RR (1977). XXX In Mackie RR (ed.) Vigilance: Theory, Operational Performance and Physiological Correlates. Plenum, New York, XXX.

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Mackie RR, Miller JC (1978). Effects of hours of service, regularity of schedules, and cargo loading on truck and bus driver fatigue (HFR-TR-1765-F). Goleta CA, Human Factors Research, Inc. (NHTSA, DOT-HS-803-799) (NTIS PB-290-957) Mackie RR, O’Hanlon JF (1977). A study of the combined effects of extended driving and heat stress on driver arousal and performance. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 537-558. McDonald N (1984). Fatigue, Safety and the Truck Driver. London, Taylor and Francis. Miller JC, Mackie RR (1980). Vigilance research and nuclear security: critical review and potential applications to security guard performance (HFR-TR-2722). Goleta CA: Human Factors Research, Inc. (National Bureau of Standards, Contract NBR-GCR-80-201) Mitler MM, Miller JC (1996). Methods of testing for sleepiness. Behavioral Medicine, 21, 171-183. Molly R, Parasuraman R (1996). Monitoring an automated system for a single failure: vigilance and task complexity effects. Human Factors, 38, 2, 311-322. O’Hanlon RR, Kelley GR (1977). Comparison of performance and physiological changes between drivers who perform well and poorly during prolonged vehicular operation. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 87-110. Parasuraman R, Molloy R, Singh IL (1993). Performance consequences of automationinduced “complacency.” International J. Aviation Psychology, 3, 1, 1-23. Parasuraman R, Mouloua M, Molloy R (1996). Effects of adaptive task allocation on monitoring of automated systems. Human Factors, 38, 4, 665-679. Poulton EC (1977) Arousing stresses increase vigilance. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 423-460. Riemersma JBJ, Sanders AF, Wildervanck C, Gaillard AW (1977). Performance decrement during prolonged night driving. In RR Mackie (ed.), Vigilance: Theory, Operational Performance, and Physiological Correlates, New York, Plenum Press, 41-58. Singh IL, Molloy R, Parasuraman R (199x). Individual differences in monitoring failures of automation. J. General Psychol., 120, 3, 357-373.

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Tsang PS, Vidulich MA (1989). Cognitive demands of automation in aviation. In RS Jensen (ed.), Aviation Psychology, Aldershot (UK), Gower Technical. Wetherell A (1986). Effects of atropine on drivers’ perceptual-motor and decision-making behavior. In JF O’Hanlon and JJ de Gier (ed.), Drugs and Driving, Taylor and Francis, London Wickens CD, Flach J (1988). Human information processing. In E Wiener and D Nagel (eds.), Human Factors in Aviation, Academic Press, New York. Wickens CD, Kessel C (1979). The effects of participatory mode and task workload on the detection of dynamic system failures. IEEE Transactions on Systems, Man and Cybernetics 9, 1, 24-34. Wiener E and Curry RE, 1980. Flight deck automation: promises and problems. Ergonomics 23:995-1011. Wiener EL (1987). Application of vigilance research: Rare, medium or well done? Human Factors, 29, 6, 725-736.

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