Human-Machine Shared Framework of Intelligent Vehicles for Collision Avoidance Based on Hybrid System Theory

Most collision accidents are caused by drivers’ errors. To more effectively assist drivers in avoiding collisions, the human-machine shared driving system has become increasingly integrated into intelligent vehicles as consumer technologies and electronics. Furthermore, in collision avoidance scenarios, existing shared systems frequently exhibit excessive intervention when implementing corrective maneuvers for driver-induced errors, thereby inadvertently elevating the driver’s workload. To address these issues, this paper proposes a novel human-machine shared framework based on hybrid system theory. The shared framework contains a trajectory planning layer and a multi-state shared control layer. At the trajectory planning layer, the optimal collision-free trajectory is selected from the candidate trajectories based on natural driving data and collision constraints. The optimal trajectory is used as the reference trajectory for collision avoidance. At the shared control layer, a shared control strategy based on a hybrid automaton model performs multi-state shared control. The proposed humanmachine interaction criterion based on driver error governs the multi-state transitions. A model predictive controller is utilized to track the reference trajectory for collision avoidance. Simulation and experimental results show that the proposed method outperforms shared control methods based on steering angle deviation and control authority allocation. It reduces the drivers physical workload and automation intervention, enhancing cooperation satisfaction.