Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
This work addresses safety and efficiency issues in autonomous driving for intersection navigation, but it appears incremental as it builds on existing reinforcement learning and attention mechanisms.
The paper tackles the challenge of multi-task intersection navigation in dense traffic for autonomous vehicles by proposing a multi-task safe reinforcement learning method with social attention and a safety layer. The results show improved safety while maintaining traffic efficiency in simulations with SUMO and CARLA.
Multi-task intersection navigation including the unprotected turning left, turning right, and going straight in dense traffic is still a challenging task for autonomous driving. For the human driver, the negotiation skill with other interactive vehicles is the key to guarantee 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 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 compare the experiments in the simulator SUMO with abundant traffic flows and CARLA with high-fidelity vehicle models, which both show that the proposed algorithm can improve safety with consistent traffic efficiency for multi-task intersection navigation.