Transition in Different Critical Situations: How Non-Driving Related Tasks Affect Drivers’ Physiological Response and Takeover Behavior After Partial Automation Silent Failures

Level-2 (L2) driving automation still has many limitations, and drivers are responsible for L2 automation safety. This study investigates the impact of the criticality of takeover situations caused by longitudinal or lateral control system failures (critical, non-critical), the failure form (silent, alert), and non-driving related tasks (NDRTs) (no task, video tasks) on drivers’ physiological responses, visual behavior, and takeover performance. Sixty-eight participants completed experiments on a motion-based simulator. Results showed that participants were more challenged to recognize faded lane lines than stationary obstacles, resulting in higher accident rates. The failure form had limited effects on participants’ physiological responses and visual behavior, but the takeover request could guide participants to recognize takeover events earlier. Participants engaged in NDRTs were also responsible for monitoring the driving environment for safety. This dual task increased their visual workload and reduced their situation awareness during automation. Additionally, distracted drivers were more difficult to detect takeover events, which worsened takeover performance and increased accident rates. Specifically, 12.5% of distracted participants were in accidents, while 6.6% of those not engaged in NDRT. In addition, this study classified distracted participants into low, medium, and high-risk groups based on their takeover performance and examined the monitoring strategy and maximum eye-off-road time (max EORT). Results suggested that monitoring frequency and max EORT are more suitable for evaluating and classifying driver monitoring behaviors in L2 driving automation. The max EORT for the three groups were 6.2 s, 9.4 s, and 13.2 s, respectively. The findings of the study provide an important reference for engineers to design L2 automation systems and driver monitoring systems to assess distracted driver risk or predict their takeover responses.