Highlights Individual differences in takeover performance after Level 3 automation failure likely depend on trait attentional control. We tested the predictive utility of 2 objective EEG indices of trait attentional control: theta/beta ratio (TBR) and theta/alpha ratio (TAR) TBR was better at predicting individual drivers’ takeover reaction time and post-takeover accident likelihood. TBR can be measured from streamlined sensor montages that are conducive for practical objective assessment.
Abstract With the advent of autonomous driving, the issue of human intervention during safety-critical events is an urgent topic of research. Supervisory monitoring, taking over vehicle control during automation failures and then bringing the vehicle to safety under time pressure are cognitively demanding tasks that pose varying difficulties across the driving population. This underpins a need to investigate individual differences (i.e., how people differ in their dispositional traits) in driver responses to automation system limits, so that autonomous vehicle design can be tailored to meet the safety-critical needs of higher-risk drivers. However, few studies thus far have examined individual differences, with self-report measures showing limited ability to predict driver takeover performance. To address this gap, the present study explored the utility of an established brain activity-based objective index of trait attentional control (frontal theta/beta ratio; TBR) in predicting driver interactions with conditional automation. Frontal TBR predicted drivers’ average takeover reaction time, as well as the likelihood of accident after takeover. Moving towards practical applications, this study also demonstrated the utility of streamlined estimates of frontal TBR measured from the forehead electrodes and from a single crown electrode, with the latter showing better fidelity and predictive value. Overall, TBR is behaviourally relevant, measurable with minimal sensors and easily computable, rendering it a promising candidate for practical and objective assessment of drivers’ neurocognitive traits that contribute to their AV driving readiness.
Objective assessment of trait attentional control predicts driver response to emergency failures of vehicular automation
2022-01-26
Article (Journal)
Electronic Resource
English
Automated driving , Automation failure , Driver behaviour , Safety risk , Individual differences , Objective assessment , Electroencephalography (EEG) , Society of Automotive Engineers , SAE , Take Over Request , TOR , Electroencephalography , EEG , Frontal Theta/Beta Ratio , TBR , Frontal Theta/Alpha Ratio , TAR , Reaction Time , RT , Non-driving Related Tasks , NDRT , Attention Deficit/Hyperactivity Disorder , ADHD , Autonomous Vehicle , AV , Variance Inflation Factor , VIF
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