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In the man-machine co-driving, most of the existing indirect cooperative shared steering control(ICSSC) strategies adopt fixed driver models and are designed based on rules. However, the fixed driver model is difficult to match with the actual situation, and the rule-based strategy is hard to be designed under the multi-dimensional feature input and the multi-objective conditions and require complicated parameters adjustment. A driver model that conforms to the driving characteristics of drivers with actual driving data is established, and an ICSSC strategy is proposed based on reinforcement learning in this paper, so as to realize the dynamic allocation of human-machine steering driving weight. Firstly, the vehicle dynamics model is established according to the vehicle longitudinal, lateral and yaw dynamics, the driver driving data is collected, and then the trajectory tracking MPC (Model Predictive Control) steering controller is designed. Secondly, DQN (Deep Q-Network), DDPG (Deep D

Model predictive controlDeepq networkDeep deterministic policy gradientTwin delayed deep deterministic policy gradientHeuristic algorithmsHuman machine systemsNdirect shared steering control strategyAn machine co drivingMulti objectiveReinforcement learningResource managementVehicle dynamicsWeight distribution