论文标题
多任务安全加固学习，用于导航密集交通中的交叉点
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
论文作者
论文摘要
多任务交叉路口导航，包括未受保护的左转，向右转动以及直奔茂密的流量仍然是自动驾驶的艰巨任务。对于人类驾驶员而言，与其他互动车辆的谈判技巧是确保安全和效率的关键。但是，很难平衡自动驾驶汽车在多任务相交导航中的安全性和效率。在本文中，我们通过社会关注来制定多任务安全的加强学习，以提高与其他交通参与者互动时的安全性和效率。具体而言，社会关注模块用于关注谈判车辆的状态。此外，将安全层添加到多任务加强学习框架中，以确保安全的谈判。我们将模拟器Sumo中的实验与大量的交通流量和CARLA与高保真车辆模型进行了比较，这两者都表明所提出的算法可以提高安全性，并在多任务交叉路口导航方面保持一致的交通效率。
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.
