This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data. A model-free and off-policy deep reinforcement learning (DRL) algorithm is employed to train the driving agent, which incorporates a carefully designed reward function and multi-task learning technique to enhance generalization across diverse driving tasks and scenarios. Experimental results demonstrate the effectiveness of the proposed approach in improving safety and efficiency in the task of traversing unsignalized intersections in mixed-autonomy traffic, and its generalizability to previously unseen scenarios, such as roundabouts. The integration of V2X communication offers a significant data source for autonomous vehicles (AVs) to perceive their surroundings beyond onboard sensors, resulting in a more accurate and comprehensive perception of the driving environment and more safe and robust driving behavior.

该论文提出了一种基于激光雷达和车联网技术的端到端自动驾驶方法(V2X-Lead)，以解决在混合自治交通条件下导航不规范的城市场景的挑战。该方法通过融合车载激光雷达传感器和V2X通信数据来处理不完整的局部观测。采用无模型和离策略深度强化学习(DRL)算法训练驾驶代理，该算法结合精心设计的奖励函数和多任务学习技术，提高不同驾驶任务和场景之间的泛化能力。实验结果表明，该方法在混合自治交通下通过无信号交叉口的任务中提高了安全性和效率，并在之前未见过的场景(如环状交叉口)中具有泛化能力。V2X通信的集成为自动驾驶车辆(AVs)提供了一个重要的数据源，使其能够在车辆感知上超越车载传感器，从而实现更准确、更全面的驾驶环境感知和更安全、更稳健的驾驶行为。

V2X-Lead:基于LiDAR的端到端自动驾驶与车联网通信集成