V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration

TL;DR

This paper introduces V2X-Lead, a LiDAR-based end-to-end autonomous driving system that integrates V2X communication and deep reinforcement learning to improve safety, perception, and generalization in complex urban traffic scenarios.

Contribution

It presents a novel fusion of LiDAR and V2X data with a model-free DRL approach for robust autonomous driving in mixed-autonomy environments.

Findings

Enhanced safety and efficiency at unsignalized intersections

Improved perception accuracy through V2X data integration

Demonstrated generalization to unseen scenarios like roundabouts

Abstract

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.