Multi-Attribute Decision-Based Global Planning for Multi-Vehicle Autonomous Parking

Abstract:

Although the autonomous valet parking technology is able to complete parking operations instead of drivers， the lack of scheduling of multiple vehicles can easily cause traffic chaos. This paper proposes a multi-attribute decision-based global planning method for multi-vehicle autonomous parking paths， which aims to solve the problem of unreasonable parking space allocation and reduce the overall cost of parking. First， a topological map of parking spaces is established to construct guided paths based on straight lines and clothoids. Then， the criteria and sub-criteria of the guided path selection are designed， and an analysis hierarchical process （AHP） path selection system is constructed to determine the optimal parking space and its guided path. Finally， the parking starting and ending positions are determined， and the parking path is calculated based on the collision constraints of the parking space using the clothoids as the reference. The scenario of multiple vehicles entering the parking lot at the same time is simulated， and the simulation results show that the method can effectively reduce the parking cost and improve the parking efficiency.