Constructing the behavioral sequence of the takeover process—TOR, behavior characteristics and phases division: A real vehicle experiment. (June 2023) - Yu, Zhenhua; Xu, Gerui; Jiang, Kang; Feng, Zhongxiang; Xu, Shan
Highlights: This study aims to verify the behavioral sequence of the behavioral process characteristics of different takeover phases through real vehicle experiments. The takeover of autonomous driving is a dynamic behavioral sequence in the four phases of the takeover process. The time sequence of takeover operations has coherent and incoherent dynamic characteristics at different takeover phases. Different types of audio prompts will affect the timing of the takeover operation and its driving performance. Abstract: Autonomous driving will still use human–machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human–machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.