An Adaptive Weight Collaborative Driving Strategy Based on Stackelberg Game Theory - Zhongjin Zhou; Jingbo Zhao; Jianfeng Zheng; Haimei Liu (World Electric Vehicle Journal)
In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion.