Mandatory Lane-Changing Decision-Making in Dense Traffic for Autonomous Vehicles based on Deep Reinforcement Learning

Mandatory lane changing in complex and crowded traffic environment is a great challenge for autonomous vehicles. This paper proposes a decision-making model based on reinforcement learning, encoded prior knowledge and traffic rules for exiting tasks within a limited distance. First, a decision policy of acceleration and lane-change actions is constructed based on the reinforcement learning technique. Also, driver prior knowledge and traffic rules are encoded to construct behavior constraints, which helps the agent explore efficiently and save training time. In addition, in order to dynamically adjust the lane-change style, the lane-change urgency that varies with the remaining distance is introduced into the framework. A challenging highway exiting scenario is designed to verify the performance of the proposed method. The results indicate that the proposed model can change the driving style according to task urgency, switching from actively overtaking and changing lanes to passively waiting for a safety gap. Compared with the traditional rule-based method, it performs better in the highway exiting task under high traffic density and limited remaining distance with a greater success rate of more than 95%.