Exploring how physio-psychological states affect drivers' takeover performance in conditional automated vehicles

Although driving automation is promised to improve driving safety, drivers are still required to take over the control of the vehicles in case of emergency. Estimating drivers' takeover performance serves as the basis for adaptive driving automation and takeover request (TOR) to ensure driving safety. However, although algorithms have been proposed to estimate drivers' takeover performance through physiological and eye-tracking measures, the complex interrelationships between these metrics and driver behavior, as well as the interactions among the metrics themselves, are not fully understood. To answer this question, a driving simulation experiment involving 42 participants was conducted. Drivers experienced three types of takeover scenarios requested by TOR while driving a conditionally automated vehicle. Drivers' physiological, eye-tracking metrics and psychological states, as imposed by several non-driving-related tasks were collected. A structural equation model was used to explore the interactions among physiological metrics (i.e., cardiac activity, respiratory activity, electrodermal activity), eye-tracking metrics, psychological states (i.e., trust in driving automation and perceived workload), and variations in takeover time and takeover quality. The results showed that trust was positively associated with takeover quality, while workload was positively associated with takeover time. Additionally, physiological and eye-tracking metrics were indirectly associated with takeover quality via psychological states. This study reveals the hierarchical relationship among takeover-performance-related variables and provides insights for designing driver monitoring systems aimed at estimating takeover performance in vehicles with driving automation and adaptive driving automation to improve driving safety.