A Novel Motion Planning for Autonomous Vehicles Using Point Cloud Based Potential Field

Ensuring accurate and efficient perception and motion planning is critical for the safety of autonomous vehicles. Addressing these pivotal challenges, this paper introduces a novel motion planning method employing a Lidar point cloud-based potential field (PF). Our approach innovatively extracts the drivable area boundary from point cloud, enhancing computational efficiency and reducing common perception errors, such as missed detections and inaccurate obstacle shape estimation. Built upon this drivable area boundary, the PF effectively represents the cost of traversing diverse areas. The PF is integrated into a model predictive control (MPC) framework to generate control commands considering vehicle dynamics, constraints, collision avoidance, and passenger comfort. Given the highly nonlinear nature of simultaneous longitudinal and lateral motion planning, an efficient Frenet frame-based trajectory sampling method is developed to provide an initial guess of the optimal trajectory for this complex motion planning task. The perception module has been validated in real bus tests, confirming its reliability and efficiency, and the entire motion planning methodology has been rigorously tested through simulations. These simulations show that our method efficiently generates smooth and safe control commands, even in challenging scenarios where the obstacle vehicle suddenly changes its lane, and remains robust under considerable state observation noise.