Reinforcement Learning based Lane Change Decision-Making with Imaginary Sampling

This paper investigates the autonomous driving lane change problem with reinforcement learning methods. A two-stage control method is proposed which includes a decision-making module computing the high-level lane change action and a lateral control module outputting the low-level steering angle. Due to the decoupling system framework, the proposed controller can flexibly cancel the previous improper lane change command. To improve the data efficiency in the lateral control module, the Gaussian process is employed to modeling the local time-interval system models. These models are used to generate imaginary samples and facilitate the reinforcement learning training process. The lateral control experiments show that the imaginary samples can improve the data efficiency and speed up the training process. Finally, the two-lane scene and three-lane scene experiments validate the effectiveness of the proposed two-stage lane change method.