User Preference-Aware and Efficient Trajectory Planning for Autonomous Parking with Hybrid A* and Nonlinear Optimization

Trajectory planning can be formulated as a nonlinear optimization problem that needs a proper initial guess as a warm-start to accelerate convergences. Current studies often ignore the users' preferences on safety and thus the distance to obstacles may either be too close or too far. Also, unnecessary gear shifting points can be caused by the local optimal but unreasonable Reeds-Shepp curve connection in the hybrid A*, degrading the user's acceptance. The existing works also suffer from high computation costs and low success rates, limiting their practical use. To tackle this, we propose an efficient user preference-aware trajectory planning framework for autonomous parking. A segmented hybrid A* is built to provide the initial guess for the nonlinear trajectory optimization. Specifically, we use A * to choose a user-preferred path considering safety and travel efficiency preferences. Then, we set guide points along the selected A * path and connect the guide points using the segmented hybrid A * to generate the coarse trajectory. In addition, safety-adaptive driving corridors are efficiently constructed considering the user's safety awareness with varying step sizes. Moreover, a local search strategy and a local optimization model are designed to optimize the unnecessary gear-shifting points. Simulation experiments demonstrate the superiority of our method in complex cases regarding safety and driving comfort. Our approach also outperforms the baseline approaches regarding the computation time and success rate.