The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.

翻译：无线技术的最新进展使得网联自动驾驶车辆（CAVs）能够通过车对车（V2V）通信获取数据，例如来自其他车辆的处理后的激光雷达（LIDAR）和摄像头数据。本文为CAVs设计了一个集成的信息共享与安全多智能体强化学习（MARL）框架，旨在利用额外信息进行决策，以提高交通效率与安全性。我们首先使用权重剪枝卷积神经网络（CNN）在每个自动驾驶车辆本地处理原始图像和点云LIDAR数据，并将CNN输出数据共享给邻近CAVs。随后，我们设计了一种安全演员-评论家算法，该算法利用车辆自身的局部观测和通过V2V通信接收的信息，探索具有安全保障的高效行为规划策略。通过在CARLA模拟器上进行实验，我们展示了该方法在不同CAV比例和不同交通密度下，能提升CAV系统在平均速度和舒适性方面的效率。我们还表明，该方法避免了不安全动作的执行，并始终与其他车辆保持安全距离。我们构建了一个转角障碍场景，以证明共享视觉信息能帮助CAVs更早发现障碍物，并采取措施避免交通拥堵。