Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety and efficiency of the CAV system in dynamic and complicated driving scenarios. In this work, we propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel Safety Shield for CAVs in challenging driving scenarios that includes unconnected hazard vehicles. The coordination mechanisms of the proposed MARL include information sharing and cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer as a spatial-temporal encoder that enhances the agent's environment awareness. The Safety Shield module with Control Barrier Functions (CBF)-based safety checking protects the agents from taking unsafe actions. We design a constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe and cooperative policies for CAVs. With the experiment deployed in the CARLA simulator, we verify the performance of the safety checking, spatial-temporal encoder, and coordination mechanisms designed in our method by comparative experiments in several challenging scenarios with unconnected hazard vehicles. Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.

翻译：通信技术使网联自动驾驶车辆（CAVs）间的协作成为可能。然而，在动态复杂的驾驶场景中，如何利用共享信息提升CAV系统的安全性与能效仍不明确。本文针对包含非网联危险车辆在内的挑战性驾驶场景，提出了一种带并行安全屏障的约束多智能体强化学习（MARL）框架。该MARL的协作机制包含信息共享与协同策略学习，并采用图卷积网络（GCN）-Transformer作为时空编码器以增强智能体的环境感知能力。基于控制屏障函数（CBF）安全检测的安全屏障模块可防止智能体执行不安全动作。我们设计了约束多智能体优势演员-评论家（CMAA2C）算法来训练CAV的安全协作策略。通过在CARLA仿真器中部署实验，我们在多个包含非网联危险车辆的挑战场景中通过对比实验验证了安全检测、时空编码器及协作机制的性能。结果表明，所提方法在挑战场景中显著提升了系统的安全性与效率。