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.

翻译：本文提出了一种基于LiDAR的端到端自动驾驶方法，该方法集成了车路协同通信技术，称为V2X-Lead，旨在解决混合自主交通条件下非管制城市场景导航的挑战。所提方法通过融合车载LiDAR传感器与V2X通信数据，处理不完美的局部观测问题。采用无模型且离策略的深度强化学习算法训练驾驶智能体，该算法结合了精心设计的奖励函数与多任务学习技术，以增强在多样化驾驶任务与场景中的泛化能力。实验结果表明，所提方法在混合自主交通中穿越无信号交叉口的任务中有效提升了安全性与效率，并能泛化至先前未见过的场景（如环形交叉口）。V2X通信的集成为自动驾驶车辆提供了超越车载传感器的感知环境的重要数据源，从而实现了对驾驶环境更准确、更全面的感知，以及更安全、更鲁棒的驾驶行为。