Research on Sampling-Based Motion Planning and Control in Spiral Ramp Scenarios of Underground Parking Garages

The slope and curvature of spiral ramps in underground parking garages change continuously, and often lacks of predefined map information. Traditional planning algorithms is difficult to ensure safety and real-time performance for autonomous vehicles entering and exiting underground parking garages. Therefore, this study proposed the Model Predictive Path Integral (MPPI) method, focusing on solving motion planning problems in underground parking garages without predefined map information. This sample-based method to allows simultaneous online autonomous vehicle planning and tracking while not relying on predefined map information,along with adjusting the driving path accordingly. Key path points in the spiral ramp environment were defined by curvature, where reducing the dimensionality of the sampling space and optimizing the computational efficiency of sampled trajectories within the MPPI framework. This ensured the safety and computational speed of the improved MPPI method in motion planning for spiral ramp environments. A co-simulation platform based on Prescan, CarSim, and MATLAB was established for constructing a spiral ramp scenario model with variable slopes and curvatures in an underground garage. Motion planning simulations used the improved MPPI method in this scenario and showed that autonomous vehicles can operate safely and efficiently in the spiral ramp environment.