Path Tracking Coordination Control With TD3 Reinforcement Learning Based on Danger Level Recognition Considering Generalized Functional Safety

In view of the generalized functional safety problems faced by intelligent vehicles, this paper focuses on vehicle driving safety and functional insufficiency of the steering system, and proposes a path tracking coordinated control method with TD3 reinforcement learning based on vehicle danger recognition. Based on the parameters of vehicle driving stability, the concept of vehicle danger level (DL) is established by integrating the parameters of path tracking accuracy. Affinity propagation (AP) clustering algorithm is used to cluster the vehicle driving status under different road adhesion coefficients, speeds and curvatures. The results of DL recognition are added to the design of TD3 reinforcement learning reward function, and the coordination control of automatic steering and differential braking under different conditions is realized, so as to achieve high-precision path tracking control under the premise of ensuring vehicle safety. The results of CarSim/Simulink co-simulation and hardware-in-loop test show that the vehicle DL can be more accurately recognized by using AP clustering algorithm and the reinforcement learning control strategy combined with DL recognition is superior to the traditional fixed parameter MPC controller and the reinforcement learning control strategy without considering the clustering method in improving the path tracking accuracy and lateral stability of the vehicle. The training convergence speed is improved in both the variable curvature road and the docking road, in which the functional insufficiency problem of the steering system is fully alleviated in the variable curvature road, and the energy loss caused by the additional yaw moment is less in the docking road.