Abstract: In order to explore a controller with better performance and practical application value for the trajectory tracking of autonomous driving, this paper applies the deep reinforcement learning algorithm of twin delayed deep deterministic policy gradient (TD3) to the lateral control of trajectory tracking. The controller design is based on the application scenario of lane line keeping. Firstly, the neural network structure and its parameters are designed based on the TD3 algorithm, and the state space and action output are defined according to the behavior of the human driver, so that it has higher training speed and better control effect. Then, a reward function is designed, which takes tracking accuracy and comfort as the optimization direction of controller performance at the same time. Finally, in order to verify the performance of the designed controller, a variety of simulation experiment scenarios were set up in Prescan to conduct simulation experiments according to the ISO 11270:2014(E) standard. In addition, the comparison with the experimental results of the current main trajectory tracking solutions proves that the controller can meet the application requirements and has better control performance in terms of tracking accuracy and comfort, and has high application value.
Keywords: autonomous driving; trajectory tracking; deep reinforcement learning; twin delayed deep deterministic policy gradient (TD3) algorithm; reward function
合肥工业大学学报自科版