From Virtual to Reality: A Deep Reinforcement Learning Solution to Implement Autonomous Driving with 3D-LiDAR

Autonomous driving technology faces significant challenges in processing complex environmental data and making real-time decisions. Traditional supervised learning approaches heavily rely on extensive data labeling, which incurs substantial costs. This study presents a complete implementation framework combining Deep Deterministic Policy Gradient (DDPG) reinforcement learning with 3D-LiDAR perception techniques for practical application in autonomous driving. DDPG meets the continuous action space requirements of driving, and the point cloud processing module uses a traditional algorithm combined with attention mechanisms to provide high awareness of the environment. The solution is first validated in a simulation environment and then successfully migrated to a real environment based on a 1/10-scale F1tenth experimental vehicle. The experimental results show that the method proposed in this study is able to complete the autonomous driving task in the real environment, providing a feasible technical path for the engineering application of advanced sensor technology combined with complex learning algorithms in the field of autonomous driving.