  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.
