Hierarchical Driving Strategy for Connected and Autonomous Vehicles Making a Protected Left Turn at Signalized Intersections

Abstract

Left-turn execution in autonomous driving at urban intersections is often complex and characterized by unpredicted events, such as vehicles speeding and running a red light. Despite these hazards, autonomous vehicles must drive through intersections safely and efficiently. To solve this problem, a new hierarchical driving strategy (HDS) is proposed for connected and autonomous vehicles making a protected left turn at signalized intersections, which combines the rule-based method and deep reinforcement learning (DRL). The high level of the HDS is the rule-based decision-making module, and the low level is the driving skill, which is dependent on the status. Specifically, the vehicle status when turning left is divided into safe and alert statuses. Further, DRL is used to train the driving skill of the vehicle for each status. The HDS design effectively combines the advantages of end-to-end driving. Moreover, the algorithm was experimentally evaluated in multiple protected left-turn scenarios. Compared with the pure rule-based method, the HDS achieved a 34% reduction in failure rate in the test scenario, and the driving behavior of the autonomous vehicle employing HDS was more intelligent. Moreover, the HDS is highly robust to complex scenarios.