How do obstacle characteristics and driver alertness affect the takeover process in conditionally automated driving?

Before the achievement of fully automated driving, drivers are still required to take control of the vehicle when necessary. The performance of this takeover process is influenced by various factors, such as the driver’s state of alertness and the characteristics of the traffic environment. This study explored how obstacle characteristics in the traffic scenario, driver alertness, and non-driving related tasks (NDRTs) affected the performance of the takeover process, particularly the situation understanding time and takeover reaction time,  in conditionally automated driving. The AdVitam dataset published by Meteier et al. (2023) was used in this study, where a driving simulation experiment was conducted with 90 participants, collecting data on electrodermal activity (EDA) and subjective perceived risk in six different scenarios (Deer, Cone, Frog, Can, False Alarm 1, and False Alarm 2). A Structural Equation Model (SEM) was constructed to investigate the causal relationship among obstacle movability, obstacle inherent hazard, NDRTs, driver perceived risk, alertness prior to the takeover request, and takeover process. The results indicated that driver alertness, measured by features extracted from EDA, played a significant role in takeover reaction time. Higher alertness leaded to quicker reactions when taking over control. Furthermore, perceived risk, influenced by obstacle movability and inherent hazard, significantly mediated the relationship between obstacle characteristics and takeover reaction time. Additionally, obstacle movability affected situation understanding time directly. These findings suggest that obstacle characteristics and driver physiological signals can be combined for an accurate prediction of drivers’ situation understanding time and takeover reaction time in automated vehicles, thereby enabling adaptive adjustment of takeover warning lead time and enhancing human–machine interaction experience during automated-to-manual transition.