Evaluating driver comprehension of the roadway environment to retain accountability of safety during driving automation

Driver comprehension is a substantial component of situation awareness that involves the ability of an individual to understand the significance of an object, traffic sign, or hazard while driving. An increase in crashes related to autonomous driving systems has raised a concern regarding the safety of other roadway users due to the diminishing accountability resulting from a general lack of understanding of the limitations or disregard of the safety protocols by users. To keep drivers vigilant when engaged in partial automated systems, a methodology to monitor real-time driver comprehension was proposed. A driving simulator study consisting of 90 participants, equally split between males and females, was executed to establish driver comprehension in six different variations of driving difficulty. Joint probability density functions were created by considering percent time spent gazing, answers to probe questions, and driving performance. Based on these density functions, five levels of comprehension were devised and assigned thresholds. Overall, as task difficulty increased, a non-linear deterioration in driving speed along with an increase in total gaze duration was observed before comprehension was attained. A two-step validation protocol was also proposed to ensure similar levels of comprehension to non-automated driving from the human driver, when engaged in early forms of automation. The proposed real-time driver comprehension monitoring constitutes a first step toward developing a methodology to reinstate the accountability of safety of other roadway users when engaged in driver-in-the-loop automation systems.

Automated summary

Driver comprehension is crucial in driving, especially when accidents related to autonomous driving systems raise safety concerns for other road users. Monitoring driver comprehension in real-time is proposed as the first step towards reinstating safety accountability in driver-in-the-loop automation systems.