Efficient Lane-changing Behavior Planning via Reinforcement Learning with Imitation Learning Initialization

Robust lane-changing behavior planning is critical to ensuring the safety and comfort of autonomous vehicles. In this paper, we proposed an efficient and robust vehicle lane-changing behavior decision-making method based on reinforcement learning (RL) and imitation learning (IL) initialization which learns the potential lane-changing driving mechanisms from driving mechanism from the interactions between vehicle and environment, so as to simplify the manual driving modeling and have good adaptability to the dynamic changes of lane-changing scene. Our method further makes the following improvements on the basis of the Proximal Policy Optimization (PPO) algorithm: (1) A dynamic hybrid reward mechanism for lane-changing tasks is adopted; (2) A state space construction method based on fuzzy logic and deformation pose is presented to enable behavior planning to learn more refined tactical decision-making; (3) An RL initialization method based on imitation learning which only requires a small amount of scene data is introduced to solve the low efficiency of RL learning under sparse reward. Experiments on the SUMO show the effectiveness of the proposed method, and the test on the CARLA simulator also verifies the generalization ability of the method.