Optimal Parking Space Selection and Vehicle Driving Decision for Autonomous Parking System Based on Multi-Attribute Decision

Abstract

Autonomous parking systems (APSs) can help drivers complete the task of finding a parking space and the parking operation, which improves driving comfort. Current research on APSs focus on the perception, localization, planning, and control modules, while few pay attention to the decision modules. This paper proposes a method for optimal parking space selection and vehicle driving decisions. In terms of selecting the optimal parking space, a multi-attribute decision method is designed considering the type of parking space, walking distance, and other factors. In terms of vehicle driving decisions, we first predict the behavior and trajectory of the target vehicle in a specific scenario, and then use a combination of rule-based and learning-based decision methods for safe and comfortable vehicle driving behavior decisions. Simulation results show that the proposed methods can find the optimal parking space according to the parking lot map and improve the efficiency and smoothness of vehicle driving while ensuring driving safety.