Multi-task safe reinforcement learning for navigating intersections in dense traffic

Yuqi Liu, Yinfeng Gao, Qichao Zhang, Dawei Ding, Dongbin Zhao

Abstract: Multi-task intersection navigation, which includes unprotected turning left, turning right, and going straight in heavy traffic, remains a difficult task for autonomous vehicles. For the human driver, negotiation skills with other interactive vehicles are the key to guaranteeing safety and efficiency. However, it is hard to balance the safety and efficiency of the autonomous vehicle for multi-task intersection navigation. In this paper, we formulate a multi-task safe reinforcement learning framework with social attention to improve the safety and efficiency when interacting with other traffic participants. Specifically, the social attention module is used to focus on the states of negotiation vehicles. In addition, a safety layer is added to the multi-task reinforcement learning framework to guarantee safe negotiation. We deploy experiments in the simulators SUMO, which has abundant traffic flows, and CARLA, which has high-fidelity vehicle models. Both show that the proposed algorithm improves safety while maintaining stable traffic efficiency for the multi-task intersection navigation problem. More details and demonstrations are available at https://github.com/liuyuqi123/SAT.