Automotive Innovation ›› 2023, Vol. 6 ›› Issue (1): 3-19.doi: 10.1007/s42154-022-00207-y
Effects of Driver Response Time Under Take-Over Control Based on CAR-ToC Model in Human–Machine Mixed Traffic Flow
Yucheng Zhao1 · Haoran Geng1 · Jun Liang1 · Yafei Wang2 · Long Chen1 · Linhao Xu3 · Wanjia Wang3
- 1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China 2. School of Mechanical and Power Engineering, Shanghai Jiaotong University, Shanghai 200000, China 3. Jiangsu Beidou Xingtong Automotive Electronics Co., Ltd, Suqian 223800, China
Effects of Driver Response Time Under Take-Over Control Based on CAR-ToC Model in Human–Machine Mixed Traffic Flow
Yucheng Zhao1 · Haoran Geng1 · Jun Liang1 · Yafei Wang2 · Long Chen1 · Linhao Xu3 · Wanjia Wang3
- 1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China 2. School of Mechanical and Power Engineering, Shanghai Jiaotong University, Shanghai 200000, China 3. Jiangsu Beidou Xingtong Automotive Electronics Co., Ltd, Suqian 223800, China
摘要： The take-over control (ToC) of human–machine interaction is a hotspot. From automatic driving to manual driving, some factors affecting driver response time have not been considered in existing models, and little attention has been paid to its effects on mixed traffic flow. This study establishes a ToC model of response based on adaptive control of thought-rational cognitive architecture (CAR-ToC) to investigate the effects of driver response time on traffic flow. A quantification method of driver’s situation cognition uncertainty is also proposed. This method can directly describe the cognitive effect of drivers with different cognitive characteristics on vehicle cluster situations. The results show that when driver response time in ToC is 4.2 s, the traffic state is the best. The greater the response time is, the more obvious the stop-and-go waves exhibit. Besides, crashes happen when manual vehicles hit other types of vehicles in ToC. Effects of driver response time on traffic are illustrated and verified from various aspects. Experiments are designed to verify that road efficiency and safety are increased by using a dynamic take-over strategy. Further, internal causes of effects are revealed and suggestions are discussed for the safety and efficiency of autonomous vehicles.