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.

翻译：多任务交叉导航,包括无防护的左转、右转和直通密交通,仍然是自主驾驶的一项艰巨任务。对于驾驶员来说,与其他交互式车辆的谈判技巧是保障安全和效率的关键。然而,很难平衡多任务交叉导航的自主车辆的安全和效率。在本文件中,我们开发了一个多任务安全强化学习,以社会关注的方式提高与其他交通参与者互动时的安全和效率。具体地说,社会关注模块用于关注谈判车辆的状况。此外,在多任务强化学习框架中增加一个安全层,以保证安全谈判。我们将模拟超载超载飞行器的实验与大量交通流量和CARLA的实验与高度忠诚车辆模型进行比较,这都表明拟议的算法可以提高安全性,使多任务交叉航行的交通效率一致。