基于逆模型预测控制的拟人驾驶控制

In this paper，a human-like driving control based on inverse model predictive control is proposed，which realizes human-like driving by updating the weight coefficients of the cost function of the control module using the loss function of the real-time trajectory generated by the model predictive control and the driver's trajectory. The human-like driving control is constructed as a two-layer optimization problem. In the lower layer，real-time state trajectories are generated by solving a typical optimal control problem using model predictive control. The optimization objective function of the lower layer is then updated by minimizing the error between the generated real-time trajectories and those of human drivers in the upper layer. The auxiliary systems based on the differential Pontryagin's Maximum Principle are constructed to solve the gradient of the weight coefficients of the cost function for the real axis trajectory. The driver's driving data are collected from the real vehicle，imitated，and tested. The results show that the method proposed in this paper，compared with two types of inverse optimal control methods based on the virtual-time trajectory，reduces the maximum error with the real trajectory by 73.52% and 65.03% in the three test conditions，with the driving behavior more anthropomorphic and has the generalization performance.