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汽车（CAV）能够通过车对车（V2V）通信收集数据，例如来自其他车辆处理过的激光雷达和摄像头数据。本文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，用于CAV在决策时利用额外信息以提高交通效率和安全性。我们首先使用权重剪枝的卷积神经网络（CNN）在每个自动驾驶汽车本地处理原始图像和点云激光雷达数据，并将CNN输出数据共享给邻近CAV。随后，我们设计了一种安全的演员-评论家算法，该算法利用车辆自身局部观测及通过V2V通信接收的信息，探索具有安全保证的高效行为规划策略。通过在CARLA模拟器上的实验，我们展示了该方法在不同CAV比例和交通密度下能够提升CAV系统的平均速度和舒适性效率。同时，该方法可避免执行不安全动作，并始终保持与其他车辆的安全距离。我们还构建了角落障碍物场景，证明共享视觉信息能帮助CAV更早观察到障碍物，从而采取行动避免交通拥堵。