Effects of platooning on signal-detection performance, workload, and stress: A driving simulator study

Driver Vigilance Decrement is More Severe During Automated Driving than Manual Driving

OBJECTIVE

The present study compared the performance, workload, and stress associated with driver vigilance in two types of vehicle: a traditional, manually operated vehicle, and a partially automated vehicle.

BACKGROUND

Drivers of partially automated vehicles must monitor for hazards that constitute automation failures and the need for human intervention, but recent research indicates that a driver's ability to do so declines as a function of time. That research lacked a comparison measure of driving without vehicle automation, so it is unknown to what degree these effects are specific symptoms of monitoring the roadway during an automated drive. Drivers in manual control of their vehicle must similarly monitor for hazards and may suffer similar vigilance decrements.

METHOD

Participants completed a simulated 40-minute drive while monitoring for hazards. Half of participants completed the drive with an automated driving system that maintained speed and lane position; the remaining half manually controlled the vehicle's speed and lane position.

RESULTS

Driver sensitivity to hazards decreased and tendency to make false alarms increased over time in the automated control condition, but not in the manual control condition. Drivers in both conditions detected fewer hazards as the drive progressed. Ratings of workload and task-induced stress were elevated similarly in both conditions.

CONCLUSION

Partially automated driving appears to uniquely impair driver vigilance by reducing the ability to discriminate between benign and dangerous events in the driving environment as the drive progresses.

APPLICATION

Applied interventions should target improvements in driver sensitivity to hazardous situations that signal potential automation failures.

Is driving experience all that matters? Drivers' takeover performance in conditionally automated driving

INTRODUCTION

In conditionally automated driving, drivers are allowed to engage in non-driving related tasks (NDRTs) and are occasionally requested to take over vehicle control in situations that the automation system cannot handle. Drivers may not be able to adequately perform such requests if they have limited driving experience. This study investigates the influence of driving experience on takeover performance in conditionally automated driving.

METHOD

Nineteen subjects participated in this driving simulator study. The NDRTs consisted of three tasks: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). These three NDRTs require drivers to invest high, moderate, and low levels of mental workload, respectively. The duration of engagement in each NDRT before a takeover request (TOR) was either 5 minutes (short interval) or 30 minutes (long interval).

RESULTS

Drivers' driving experience and performance during the control period are highly correlated with their TOR performance. Furthermore, the type and duration of NDRT influence TOR performance, and inexperienced drivers exhibit poorer TOR performance than experienced drivers.

CONCLUSIONS AND PRACTICAL APPLICATIONS

These findings have relevance for the types of NDRTs that ought to be permitted during automated driving, the design of automated driving systems, and the formulation of regulations regarding the responsible use of automated 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.

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

How does eco-driving make us feel? Considering the psychological effects of eco-driving

Despite both the environmental and financial benefits of eco-driving being well known, the psychological impact of engaging in eco-driving behaviours has received less attention within the literature. It was anticipated that being asked to engage in eco-driving behaviours not only has an impact on vehicle fuel usage, but also on the driver, both in terms of their overall mood and willingness to re-engage with the task at a later time. Results from a simulated driving study suggest that although eco-driving was beneficial in reducing fuel consumption, being asked to eco-drive had a negative effect on overall journey time and mood. Engaging in eco-driving did however have a positive effect on self-esteem, suggesting potential longer term psychological benefits of adopting this behaviour.

Detecting driver stress and hazard anticipation using real-time cardiac measurement: A simulator study

OBJECTIVES

In the context of growing interest in real-time driver stress detection systems, we question the value of using heart rate change over short time periods to detect driver stress and hazard anticipation.

METHODS

To this end, we explored changes in heart rate and speed as well as perceived stress in 27 drivers in a driving simulator. Driver stress was triggered by using hazardous road events, while hazard anticipation was manipulated using three levels of hazard predictability: unpredictable (U), predictable (P), and predictable and familiar (PF).

RESULTS

The main results indicate that using heart rate change (1) is a good indicator for detecting driver stress in real time, (2) provides a cardiac signature of hazard anticipation, and (3) was affected by perceived stress groups. Further investigation is needed to validate the lack of relationship between increased anticipation/predictability and strengthened cardiac signature.

CONCLUSIONS

These results support the use of heart rate change as an indicator of real-time driver stress and hazard anticipation.

Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review

Background: The link between driving performance impairment and driver stress is well-established. Identifying and understanding driver stress is therefore of major interest in terms of safety. Although many studies have examined various physiological measures to identify driver stress, none of these has as yet been definitively confirmed as offering definitive all-round validity in practice. Aims: Based on the data available in the literature, our main goal was to provide a quantitative assessment of the sensitivity of the physiological measures used to identify driver stress. The secondary goal was to assess the influence of individual factors (i.e., characteristics of the driver) and ambient factors (i.e., characteristics of the context) on driver stress. Age and gender were investigated as individual factors. Ambient factors were considered through the experimental apparatus (real-road vs. driving simulator), automation driving (manual driving vs. fully autonomous driving) and stressor exposure duration (short vs. long-term). Method: Nine meta-analyses were conducted to quantify the changes in each physiological measure during high-stress vs. low-stress driving. Meta-regressions and subgroup analyses were performed to assess the moderating effect of individual and ambient factors on driver stress. Results: Changes in stress responses suggest that several measures are sensitive to levels of driver stress, including heart rate, R-R intervals (RRI) and pupil diameter. No influence of individual and ambient factors was observed for heart rate. Applications and Perspective: These results provide an initial guide to researchers and practitioners when selecting physiological measures for quantifying driver stress. Based on the results, it is recommended that future research and practice use (i) multiple physiological measures, (ii) a triangulation-based methodology (combination of measurement modalities), and (iii) a multifactorial approach (analysis of the interaction of stressors and moderators).

Using in-seat electrical potential sensors for non-contact monitoring of heart rate, heart rate variability, and heart rate recovery

Detecting transient changes in heart rate and heart rate variability during experimental simulated autonomous driving scenarios can indicate participant arousal and cognitive load, providing valuable insights into the relationship between human and vehicle autonomy. Successfully detecting such parameters unobtrusively may assist these experimental situations as well as naturalistic driver monitoring systems within an autonomous vehicle. However, non-contact sensors must collect reliable and accurate signals. This study aims to compare the in-seat, non-contact Plessey EPIC sensor to the gold standard, contact Biopac sensor. Thirty participants took part in five-minute simulated autonomous vehicle journeys in a city environment and a rural environment, and a five-minute resting condition. To ensure the seat sensor was sensitive to elevated heart rate values, heart rate was also collected following the energetic Harvard Step Test. Lin concordance coefficients and Bland-Altman analyses were employed to assess the level of agreement between the non-contact Plessey EPIC sensor and the contact Biopac sensor for heart rate and heart rate variability. Analyses revealed a high level of agreement (r_c > 0.96) between both sensors for one-minute averaged heart rate and five-minute averaged heart rate variability during simulated autonomous driving and rest, and one-minute averaged heart rate following the Harvard Step Test. In addition, the non-contact sensor was sensitive to significant differences during tasks. This proof of principle study demonstrates the feasibility of using the non-contact Plessey EPIC sensor to accurately detect heart rate and heart rate variability during simulated autonomous driving environments.

Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review

Most accidents in the aviation, maritime, and construction industries are caused by human error, which can be traced back to impaired mental performance and attention failure. In 1596, Du Laurens, a French anatomist and medical scientist, said that the eyes are the windows of the mind. Eye tracking research dates back almost 150 years and it has been widely used in different fields for several purposes. Overall, eye tracking technologies provide the means to capture in real time a variety of eye movements that reflect different human cognitive, emotional, and physiological states, which can be used to gain a wider understanding of the human mind in different scenarios. This systematic literature review explored the different applications of eye tracking research in three high-risk industries, namely aviation, maritime, and construction. The results of this research uncovered the demographic distribution and applications of eye tracking research, as well as the different technologies that have been integrated to study the visual, cognitive, and attentional aspects of human mental performance. Moreover, different research gaps and potential future research directions were highlighted in relation to the usage of additional technologies to support, validate, and enhance eye tracking research to better understand human mental performance.

Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

Partially automated vehicle technology is increasingly common on-road. While this technology can provide safety benefits to drivers, it also introduces new concerns about driver attention. In particular, during partially automated driving (PAD), drivers are expected to stay vigilant so they can readily respond to important events in their environment. However, using partially automated vehicles on the highway places drivers in monotonous situations and requires them to do very little. This can place the driver in a state of cognitive underload in which they experience a very small amount of cognitive demand. In this situation, drivers can exhibit vigilance decrements which impact their ability to respond to on-road threats. This is of particular concern in situations when the partially automated vehicle fails to respond to a potentially critical situation and leaves all responsibility to safely navigate to the driver. This paper reviews situations that lead to vigilance decrements and characterizes the different methodologies of measuring driver vigilance during PAD, highlighting their advantages and limitations. Based on our reading of the literature, we summarize several factors future research on vigilance decrements in PAD should consider.

When virtuality becomes real: Relevance of mental abilities and age in simulator adaptation and dropouts

Previous studies increasingly report problems with simulator adaptation as well as dropouts. Therefore, the present study aims at better understanding these aspects by considering individual factors, such as age and mental abilities. 414 people were tested with commonly used neuropsychological measures as well as within a driving simulator which consists of a close-to-production vehicle of the compact class. In contrast to previous findings, neither a significant relationship between age and the time of adaptation nor an interaction between age and mental abilities on adaptation time could be identified. However, the time participants spent in the simulator (simulator dropout) significantly correlated with age but not with mental abilities. People who showed no adaptation spent significantly less time in the simulator, because of the occurrence of simulator sickness. Although attention was only mildly associated with the time of simulator adaptation, further research on this linkage is suggested. Practitioner summary: The study at hand clarifies the relevance of considering the process of simulator adaptation within simulator studies. However, the present findings suggest no relation between age and the time of adaptation but with simulator dropouts. Abbreviations: TMT: trail making test; LPS: leistungsprüfsystem; IOP: index of performance; ALFASY: altersgerechte fahrerassistenzsystem (Age-based Driver Assistance Systems).

Breaking the cycle of frustration: Applying Neisser's Perceptual Cycle Model to drivers of semi-autonomous vehicles

Semi-autonomous cars are already on the road and highly autonomous cars will soon be with us. Little is understood about how drivers will adapt to the changing relationship with their vehicle, but to ensure safety and consumer acceptance, this insight is vital. To this end, an on-road study in a semi-autonomous vehicle was undertaken with six UK drivers. The 'think aloud' technique was employed and video and audio footage of their interaction with the vehicle was captured. Neisser's (1976) Perceptual Cycle Model (PCM) was used to analyse the data and three case studies are presented to highlight how poor synergy between driver and semi-autonomous vehicles can occur from the lens of Schema, Action or World information. Seven key design considerations are proposed to ensure a more positive and safer interaction between driver and autonomous vehicle to guide focus by manufacturers. Further evidence for the existence of a 'counter cycle' (Plant and Stanton, 2015) within the PCM is found and how this relates to the challenges of using verbal protocals expressed during a fast moving dynamic task is discussed.

Dangerous intersections? A review of studies of fatigue and distraction in the automated vehicle

The impacts of fatigue on the vehicle driver may change with technological advancements including automation and the increasing prevalence of potentially distracting in-car systems. This article reviews the authors' simulation studies of how fatigue, automation, and distraction may intersect as threats to safety. Distinguishing between states of active and passive fatigue supports understanding of fatigue and the development of countermeasures. Active fatigue is a stress-like state driven by overload of cognitive capabilities. Passive fatigue is produced by underload and monotony, and is associated with loss of task engagement and alertness. Our studies show that automated driving reliably elicits subjective symptoms of passive fatigue and also loss of alertness that persists following manual takeover. Passive fatigue also impairs attention and automation use in operators of Remotely Piloted Vehicles (RPVs). Use of in-vehicle media has been proposed as a countermeasure to fatigue, but such media may also be distracting. Studies tested whether various forms of phone-based media interacted with automation-induced fatigue, but effects were complex and dependent on task configuration. Selection of fatigue countermeasures should be guided by an understanding of the form of fatigue confronting the operator. System design, regulation of level of automation, managing distraction, and selection of fatigue-resilient personnel are all possible interventions for passive fatigue, but careful evaluation of interventions is necessary prior to deployment.

What determines drivers' speed? A replication of three behavioural adaptation experiments in a single driving simulator study

We conceptually replicated three highly cited experiments on speed adaptation, by measuring drivers' experienced risk (galvanic skin response; GSR), experienced task difficulty (self-reported task effort; SRTE) and safety margins (time-to-line-crossing; TLC) in a single experiment. The three measures were compared using a nonparametric index that captures the criteria of constancy during self-paced driving and sensitivity during forced-paced driving. In a driving simulator, 24 participants completed two forced-paced and one self-paced run. Each run held four different lane width conditions. Results showed that participants drove faster on wider lanes, thus confirming the expected speed adaptation. None of the three measures offered persuasive evidence for speed adaptation because they failed either the sensitivity criterion (GSR) or the constancy criterion (TLC, SRTE). An additional measure, steering reversal rate, outperformed the other three measures regarding sensitivity and constancy, prompting a further evaluation of the role of control activity in speed adaptation. Practitioner Summary: Results from a driving simulator experiment suggest that it is not experienced risk, experienced effort or safety margins that govern drivers' choice of speed. Rather, our findings suggest that steering reversal rate has an explanatory role in speed adaptation.

Psychosocial factors associated with intended use of automated vehicles: A simulated driving study

This study applied the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to assess drivers' intended use of automated vehicles (AVs) after undertaking a simulated driving task. In addition, this study explored the potential for trust to account for additional variance to the psychosocial factors in TPB and TAM. Seventy-four participants (51% female) aged between 25 and 64 years (M = 42.8, SD = 12.9) undertook a 20 min simulated experimental drive in which participants experienced periods of automated driving and manual control. A survey task followed. A hierarchical regression analysis revealed that TPB constructs; attitude toward the behavior, subjective norms, and perceived behavioral control, were significant predictors of intentions to use AV. In addition, there was partial support for the test of TAM, with ease of use (but not usefulness) predicting intended use of AV (SAE Level 3). Trust contributed variance to both models beyond TPB or TAM constructs. The findings provide an important insight into factors that might reflect intended use of vehicles that are primarily automated (longitudinal, lateral, and manoeuvre controls) but require and allow drivers to have periods of manual control.

Is partially automated driving a bad idea? Observations from an on-road study

The automation of longitudinal and lateral control has enabled drivers to become "hands and feet free" but they are required to remain in an active monitoring state with a requirement to resume manual control if required. This represents the single largest allocation of system function problem with vehicle automation as the literature suggests that humans are notoriously inefficient at completing prolonged monitoring tasks. To further explore whether partially automated driving solutions can appropriately support the driver in completing their new monitoring role, video observations were collected as part of an on-road study using a Tesla Model S being operated in Autopilot mode. A thematic analysis of video data suggests that drivers are not being properly supported in adhering to their new monitoring responsibilities and instead demonstrate behaviour indicative of complacency and over-trust. These attributes may encourage drivers to take more risks whilst out on the road.

Assessment selection in human-automation interaction studies: The Failure-GAM2E and review of assessment methods for highly automated driving

Highly automated driving will change driver's behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAM²E model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAM²E includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAM²E in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAM²E and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction.