Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach.