Safety‐Critical Kinematically‐Executable Overtake Planning via Contingency Path‐Speed Iterative Algorithm for Automated Valet Parking ^*

ABSTRACT Autonomous driving has emerged as a highly topical subject within the realm of intelligent transportation systems. Automated valet parking (AVP) represents one of the initial mass‐production application scenarios. However, motion planning in AVP confronts a series of formidable challenges. These challenges include a constricted movement space, vehicles parked in violation of regulations, and vehicles that intrude suddenly. In response to these issues, this article devises a safety‐critical, kinematically executable overtaking planning system for AVP through a contingency path‐speed iterative algorithm. A path‐speed iterative optimisation framework is adopted, taking into full account both the curvature constraint and the contour constraint. The prediction probability of dynamic obstacles is incorporated into the quadratic optimisation problem, presented in the form of either soft or hard constraints. Furthermore, a contingency path‐speed iterative planner is formulated to address the multi‐modal predictions and the interframe probability transfer that occur during the overtaking process in parking lots. Numerical simulations (conducted on the Carla simulator with a 10 Hz planning cycle) across four complex AVP scenarios demonstrate that the proposed algorithm outperforms the baseline Baidu Apollo EM Planner. On‐road experiments (deployed on a mass‐produced MCU) further validate that the algorithm maintains real‐time performance (average computation time < 10 ms) and reduces speed oscillation by over 50% compared to the baseline, while ensuring kinematically executable trajectories (max steering wheel angle limited to 389°). These results confirm the proposed algorithm significantly enhances overtaking safety, executability, and efficiency for AVP.