Abstract:

A lane change tracking control model was proposed based on the deep reinforcement learning algorithm and simulation experiments were carried out to solve the problem of safe lane change for automatic driving vehicles on highways. A model of the vehicle lane change path was built by using a quintuple polynomial approach with the tracking error functions. A three-degree-of-freedom vehicle dynamics model was fused with the deep reinforcement learning framework to build the lane change path tracking control model, which was updated by a deep deterministic policy gradient (DDPG) algorithm. The steering angle was learned for optimal lane change path tracking to control the vehicle complete the lane change process. The results show that at a speed of 100 km/h, the maximum value of the lateral position error absolute value is close to 0 with the maximum value of the angular deviation absolute value of 10 mrad controlled by using the proposed method; The lateral position error and angular error by using the proposed trajectory tracking method are smaller than that by using the traditional model prediction control method. Therefore, this model can achieve the lane change process autonomously in a high-speed environment, which is meaningful for ensuring traffic safety and alleviating traffic.

Key words: automatic driving vehicles, lane change model, path tracking, deep reinforcement learning, quintic polynomials, deep deterministic policy gradient (DDPG) algorithm