A game-theoretic adaptive human-machine shared steering control method considering safety-assured conflict resolution

Human-machine shared steering control (SSC) has emerged as a focal area of research due to its advantages in improving driving safety and reducing driver workload. However, coordinating behaviors between the human driver and the automatic controller remains a crucial challenge when human-machine conflicts (HMC) arise. To address this challenge, a game-theoretic adaptive human-machine SSC method considering safety-assured HMC resolution is researched in this paper. Firstly, the dynamic interaction model between the human driver and the automatic controller is built using Distributed Model Predictive Control (DMPC). Notably, the automatic controller anticipates and actively accommodates the driver’s potential behaviors in this interaction model. Subsequently, the Nash equilibrium between the human driver and the automatic controller is solved to coordinate their interactive behaviors. Secondly, to ensure driving safety, a driving authority allocation strategy is provided via Potential Field Model Predictive Control (PF-MPC). On that basis, the risk potential field of the driving environment is incorporated into the cost function of PF-MPC, thereby optimizing the allocation of driving authority. Finally, the driver-in-the-loop experiments are carried out to compare the proposed method with the latest SSC techniques. The experimental results demonstrate that the proposed approach not only significantly reduces HMC, but also alleviates the driver’s physical and mental workload while enhancing driving safety and stability.