On investigating drivers' attention allocation during partially-automated driving.

The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers' ability to supervise the automated system and resume manual control of the vehicle.

On the impact of on-road partially-automated driving on drivers' cognitive workload and attention allocation.

The use of partially-automated or SAE level-2 vehicles is expected to change the role of the human driver from operator to supervisor, which may have an effect on the driver's workload and visual attention. In this study, 30 Ontario drivers operated a vehicle in manual and partially-automated mode. Cognitive workload was measured by means of the Detection Response Task, and visual attention was measured by means of coding glances on and off the forward roadway. No difference in cognitive workload was found between driving modes. However, drivers spent less time glancing at the forward roadway, and more time glancing at the vehicle's touchscreen. These data add to our knowledge of how vehicle automation affects cognitive workload and attention allocation, and show potential safety risks associated with the adoption of partially-automated driving.

Comparison of concurrent cognitive load measures during n-back tasks.

The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n-back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load.

Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations.

OBJECTIVE: This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload. BACKGROUND: Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic. METHOD: This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation. RESULTS: The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms. CONCLUSION: There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state. APPLICATION: Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

Preface to the Special Issue on Assessment and Effectiveness of Driver Monitoring Systems.

With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.

Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.

OBJECTIVE: This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems. BACKGROUND: Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven. METHOD: Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods. RESULTS: Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found. CONCLUSION: Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo. APPLICATION: The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.

On the Effect of COVID-19 on Drivers' Behavior: A Survey Study.

COVID-19 had a disruptive effect on the global community. This study looks at the effects that the stringent lockdown measures enacted in March 2020 had on motorists' driving patterns. In particular, given the greater portability of remote working associated with the drastic decline in personal mobility, it is hypothesized that these may have served as accelerators for distracted and aggressive driving. To answer these questions, an online survey was conducted in which 103 respondents were asked to report on their own and other drivers' driving behavior. While respondents agreed they drove less frequently, they also indicated that they were not prone to more aggressive driving or engaging in potentially distracting activities whether for work or personal purposes. When asked to report on other motorists' behavior, however, respondents indicated they had witnessed more aggressive and distracting drivers on the road after March 2020 relative to the time before the pandemic. These findings are reconciled with the existing literature on self-monitoring and self-enhancement bias, and the existing literature on the effect of comparable large-scale, disruptive events on traffic patterns is used to discuss the hypothesis on how driving patterns may change after the pandemic.

On the potential of pupil size as a metric of physical fatigue during a repeated handle push/pull task.

Force output and muscle activity represent the gold standards for measuring physical fatigue. This study explores using ocular metrics for tracking changes in physical fatigue during the completion of a repeated handle push/pull task. Participants completed this task over three trials, and pupil size was recorded by means of a head-mounted eye-tracker. Blink frequency was also measured. Force impulse and maximum peak force were used as ground-truth measures of physical fatigue. As expected, a reduction in peak force and impulse was observed over time as participants became more fatigued. More interestingly, pupil size was also found to decrease from trial 1 through trial 3. No changes in blink rate were found with increasing physical fatigue. While exploratory in nature, these findings add to the sparse literature exploring the use of ocular metrics in Ergonomics. They also advance the use of pupil size as a possible future alternative for physical fatigue detection.

Distracted worker: Using pupil size and blink rate to detect cognitive load during manufacturing tasks.

This study sets out to extend the use of blink rate and pupil size to the assessment of cognitive load of completing common automotive manufacturing tasks. Nonoptimal cognitive load is detrimental to safety. Existing occupational ergonomics approaches come short of measuring dynamic changes in cognitive load during complex assembling tasks. Cognitive demand was manipulated by having participants complete two versions of the n-back task (easy, hard). Two durations of the physical task were also considered (short, long). Pupil size and blink rate increased under greater cognitive task demand. High cognitive load also resulted in longer task completion times, and higher ratings of mental and temporal demand, and effort. This exploratory study offers relevant insights on the use of ocular metrics for cognitive load assessment in occupational ergonomics. While the existing eye-tracking technology may yet limit their adoption in the field, they offer advantages over the more popular expert-based and self-reported techniques in measuring changes in cognitive load during dynamic tasks.

On the Cost of Detection Response Task Performance on Cognitive Load.

OBJECTIVE: This study investigates the cost of detection response task performance on cognitive load. BACKGROUND: Measuring system operator's cognitive load is a foremost challenge in human factors and ergonomics. The detection response task is a standardized measure of cognitive load. It is hypothesized that, given its simple reaction time structure, it has no cost on cognitive load. We set out to test this hypothesis by utilizing pupil diameter as an alternative metric of cognitive load. METHOD: Twenty-eight volunteers completed one of four experimental tasks with increasing levels of cognitive demand (control, 0-back, 1-back, and 2-back) with or without concurrent DRT performance. Pupil diameter was selected as nonintrusive metric of cognitive load. Self-reported workload was also recorded. RESULTS: A significant main effect of DRT presence was found for pupil diameter and self-reported workload. Larger pupil diameter was found when the n-back task was performed concurrently with the DRT, compared to no-DRT conditions. Consistent results were found for mental workload ratings and n-back performance. CONCLUSION: Results indicate that DRT performance produced an added cost on cognitive load. The magnitude of the change in pupil diameter was comparable to that observed when transitioning from a condition of low task load to one where the 2-back was performed. The significant increase in cognitive load accompanying DRT performance was also reflected in higher self-reported workload. APPLICATION: DRT is a valuable tool to measure operator's cognitive load. However, these results advise caution when discounting it as cost-free metric with no added burden on operator's cognitive resources.

Overloaded and at Work: Investigating the Effect of Cognitive Workload on Assembly Task Performance.

OBJECTIVE: This study investigates the effect of cognitive overload on assembly task performance and muscle activity. BACKGROUND: Understanding an operator's cognitive workload is an important component in assessing human-machine interaction. However, little evidence is available on the effect that cognitive overload has on task performance and muscle activity when completing manufacturing tasks. METHOD: Twenty-two volunteers completed an assembly task while performing a secondary cognitive task with increasing levels of demand (n-back). Performance in the assembly task (completion times, accuracy), muscle activity recorded as integrated electromyography (EMG), and self-reported workload were measured. RESULTS: Results show that the increasing cognitive demand imposed by the n-back task resulted in impaired assembly task performance, overall greater muscle activity, and higher self-reported workload.Relative to the control condition, performing the 2-back task resulted in longer assembly task completion times (+10 s on average) and greater integrated EMG for flexor carpi ulnaris, triceps brachii, biceps brachii, anterior deltoid, and pectoralis major. CONCLUSION: This study demonstrates that working under high cognitive load not only results in greater muscle activity, but also affects assembly task completion times, which may have a direct effect on manufacturing cycle times. APPLICATION: Results are applicable to the assessment of the effects of high cognitive workload in manufacturing.

Precision teaching to improve drivers' lane maintenance.

INTRODUCTION: This study investigates the effect of precision teaching signals on lane maintenance. METHODS: In experiment 1, the control group drove a simulator with no signals. In experiment 2, drivers were presented with auditory signals depending on their position within or outside the lane. In experiment 3, visual signals were presented in addition to auditory signals to examine the effect of redundancy on drivers' lane maintenance. RESULTS: Results showed an improvement in lane maintenance in experiment 2. Cross-experiment analysis indicated this effect not to be the result of learning. Data from experiment 3 also showed that presenting redundant signals did not further reduce lane variability or help drivers maintain a more central position within the lane. CONCLUSIONS: Taken together, data suggest precision teaching be effective as an educational tool to improve lane maintenance. Practical Applications: Our study shows the potential for precision teaching to serve as a valuable tool in driver training.