OBJECTIVE: The authors discuss empirical studies of human-automation interaction and their implications for automation design. BACKGROUND: Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years. METHODS: Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation. RESULTS: Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven. CONCLUSIONS: The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems. APPLICATION: This research can be applied to most current and future automated systems. Language: en

Humans : Still Vital After All These Years of Automation

Driver fatigue is an ill-defined term in the literature. It has been broadly used to refer to a wide range of driver states, each with different causal mechanisms. Technologies currently exist which enable detection of driver fatigue and interventions that have the potential to dramatically reduce crash probability. The successful implementation of these technologies depends on the cause and type of fatigue experienced. Sleep-related (SR) forms of driver fatigue result from accumulated sleep debt, prolonged wakefulness or troughs in the circadian rhythms. SR fatigue is resistant to most intervention strategies. Conversely, technologies for detecting and countering task-related (TR) fatigue (caused by mental overload or underload) are proving to be effective tools for improving transportation safety. Methods of detecting and counteracting the various forms of driver fatigue are discussed. Emphasis is placed on examining the effectiveness of existing and emerging technologies for combating TR forms of driver fatigue.

Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies

This review of the literature examines, in a quantitative fashion, how the level of imperfection or unreliability of diagnostic automation affects the performance of the human operator who is jointly consulting that automation and the raw data itself. The data from 20 different studies were used to generate 35 different data points that compared performance with varying levels of unreliability, with that of a non-automated baseline condition. A regression analysis of benefits/costs relative to baseline was carried out, and revealed a strong linear function of benefits with reliability. The analysis revealed that a reliability of 0.70 was the ‘crossover point’ below which unreliable automation was worse than no automation at all. The analysis also revealed that performance was more strongly affected by reliability in high workload conditions, implicating the role of workload-imposed automation dependence in producing this relationship, and suggesting that humans tend to protect performance of concurrent ta...

The benefits of imperfect diagnostic automation: a synthesis of the literature

The trust placed in diagnostic aids by the human operator is a critical psychological factor that influences operator reliance on automation. Studies examining the nature of human interaction with automation have revealed that users have a propensity to apply norms of human–human inter-personal interaction to their interaction with ‘intelligent machines’. Nevertheless, there exist subtle differences in the manner in which humans perceive and react to automated aids compared to human team-mates. In the present paper, the concept of trust in human–automation dyads is compared and contrasted with that of human–human dyads. A theoretical framework that synthesizes and describes the process of trust development in humans vs automated aids is proposed and implications for the design of decision aids are provided. Potential implications of this research include the improved design of decision support systems by incorporating features into automated aids that elicit operator responses mirroring responses in human...

Similarities and differences between human–human and human–automation trust: an integrative review

How should we assess the comparability of driving on a road and “driving” in a simulator? If similar patterns of behaviour are observed, with similar differences between individuals, then we can conclude that driving in the simulator will deliver representative results and the advantages of simulators (controlled environments, hazardous situations) can be appreciated. To evaluate a driving simulator here we compare hazard detection while driving on roads, while watching short film clips recorded from a vehicle moving through traffic, and while driving through a simulated city in a fully instrumented fixed-base simulator with a 90 degree forward view (plus mirrors) that is under the speed/direction control of the driver. In all three situations we find increased scanning by more experienced and especially professional drivers, and earlier eye fixations on hazardous objects for experienced drivers. This comparability encourages the use of simulators in drivers training and testing.

Driving simulator validation with hazard perception

Many new in-vehicle systems focus on accident prevention by facilitating the driving task. One such driving aid is an in-vehicle collision avoidance warning system (IVCAWS), used to alert the driver to an impending collision. Our study evaluated the effects of an imperfect IVCAWS both on driver headway maintenance and on driver behavior in response to warning system errors. Our results showed that drivers tend to overestimate their headway and consequently drive with short and potentially dangerous headways, and that IVCAWSs are a useful tool for educating drivers to estimate headway more accurately. Moreover, our study showed that after a relatively short exposure to the system, drivers were able to maintain longer and safer headways for at least six months. The practical implications of these results are that the use of an IVCAWS should be considered for inclusion in driver education and training programs.

Effects of an in-vehicle collision avoidance warning system on short- and long-term driving performance

This 2-part study focuses on eye movements to explain driving-related visual performance in younger and older persons. In the first task, participants' eye movements were monitored as they viewed a traffic scene image with a numeric overlay and visually located the numbers in their sequential order. The results showed that older participants had significantly longer search episodes than younger participants, and that the visual search of older adults was characterized by more fixations and shorter saccades, although the average fixation durations remained the same. In the second task, participants viewed pictures of traffic scenes photographed from the driver's perspective. Their task was to assume the role of the driver and regard the image accordingly. Results in the second task showed that older participants allocated a larger percentage of their visual scan time to a small subset of areas in the image, whereas younger participants scanned the images more evenly. Also, older participants revisited the same areas and younger participants did not. The results suggest how aging might affect the efficacy of visual information processing. Potential applications of this research include training older drivers for a more effective visual search, and providing older drivers with redundant information in case some information is missed.

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Eye Movements of Younger and Older Drivers

An experiment was conducted to determine the effects of an in-vehicle collision avoidance warning system (IVCAWS) on driver performance. A driving simulator was driven by 135 licensed drivers. Of these, 120 received alerts from the IVCAWS when their headway to a lead car was less than 2 s, and the other 15 (the control group) received no alerts. Drivers received varied alert interfaces: auditory, visual, and multimodal. The system had varied levels of reliability, determined by both false alarm rate and failure of the IVCAWS to alert to short headway. Results indicated that the IVCAWS led to safer (longer) headway maintenance. High false alarm rates induced drivers to slow down unnecessarily; large numbers of missed alerts did not have any significant impact on drivers. Driver acceptance of the system was mixed. Interface played a role in driver reliance on the system, with the multimodal interfaces generating least reliance. Actual or potential applications of this research include IVCAWS interface selection for greater system efficacy and user acceptance and the advisability of implementation, even of imperfect systems, for drivers who seek to maintain a safer headway.

Imperfect In-Vehicle Collision Avoidance Warning Systems Can Aid Drivers

Target acquisition tasks in natural environments are often augmented by cuing systems that advise human observers during the decision process. With present technological limitations, cuing systems are imperfect, so the question arises whether cuing aids should be implemented under all conditions. We examined target acquisition performance under different levels of task complexity and cuing system reliability. We introduce here two new methods to help define observer behavior trends in cued target acquisition: a quantitative measure of observer search behavior in a temporal sense and a measure of the extent of observer reliance on the cue. We found that observer reliance on the cue correlated with task difficulty and the perceived reliability of the cue. Cuing was generally helpful in complex tasks, whereas cuing reduced performance in easy tasks. Consequently, cuing systems should be implemented only when the task is difficult enough to warrant the intrusion of a cue into the task. Actual or potential applications of this research include the design and implementation of imperfect automated aids dealing with augmented reality.

New alternative methods of analyzing human behavior in cued target acquisition.

The purpose of this study was to evaluate the efficacy of a type of in-vehicle collision avoidance warning system (IVCAWS) under conditions of driver distraction. Forty-three participants responded to an imperfect warning system while simultaneously driving a simulator and performing a visual/cognitive task. The major concerns were whether drivers would be more inclined to rely on such a system when they are distracted by subsidiary tasks, and if this reliance would be counterproductive. We found that distracted drivers responded, by increasing their temporal headway, to the less reliable system's alarms, but the warning system at the higher reliability levels led to over reliance and ultimately to maintaining shorter headways. This study has practical implications for the use of warning systems as driving aids for drivers. Although aids may be helpful and, in many cases, the more reliable aid is preferable, in the case of distraction, drivers may misuse the aid. Keywords: Driver distraction Language: en

Masha Maltz

Papers

Effects of an in-vehicle collision avoidance warning system on short- and long-term driving performance

Eye Movements of Younger and Older Drivers

Imperfect In-Vehicle Collision Avoidance Warning Systems Can Aid Drivers

New alternative methods of analyzing human behavior in cued target acquisition.

Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers