A Mathematical Modeling Approach for Optimal Parking Space Selection and Path Planning in Autonomous Parking Systems with UAV-Assisted Topsis Entropy Weight Method

Abstract

Abstract With the advancements in vehicle sensors and wireless communication technologies, the field of driverless technology has gained significant attention. However, existing studies have primarily focused on unmanned controlled parking once the vehicle reaches the vicinity of the parking space. This paper aims to address the challenges associated with optimal parking space selection and path planning for automatic parking. To achieve this, we propose an optimal planning model based on the Topsis entropy weight method, incorporating UAV-assisted scheduling. We establish a state equation-based parking model utilizing the Ackerman structure of the vehicle, enabling effective parking path planning for three distinct types of parking spaces. Through experimental results, we demonstrate the efficacy of the proposed method in resolving the problem of optimal parking space selection in parking lots, as well as overcoming difficulties encountered during vehicle parking, such as narrow parking spaces and constraints imposed by the driver's angle. This research contributes to the advancement of autonomous parking systems, enhancing their efficiency and effectiveness in real-world applications.