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In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System