Title: A priori map-based automated valet parking with accurate adjustment ability for automatic charging

Authors: Yongsheng Wang; Yugong Luo; Weiwei Kong; Dexu Bu; Yanchen Ku; Fachao Jiang

Addresses: College of Engineering, China Agricultural University, Beijing, 100083, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China ' College of Engineering, China Agricultural University, Beijing, 100083, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China ' College of Engineering, China Agricultural University, Beijing, 100083, China

Abstract: With the development and application of automatic charging system for electric vehicles, there is a demand for increasing the accuracy of vehicle parking position. In this paper, a priori map-based automated valet parking (AVP) system with accurate adjustment ability is proposed. Firstly, the parking lot topological map is designed as a priori information for path planning. Then, a path coordination and optimisation strategy is applied to merge the global path and the parking path without transition curve. After that, a "Forward Orientation and Reverse Lateral Position Deviations (FORLPD)" control strategy is proposed to accurately adjust the connecting position of automatic charging system. This strategy uses the heading angle deviation and lateral position deviation for vehicle forward and reverse motion control, separately. Finally, experimental results on a real vehicle show that longitudinal and lateral deviations of vehicle parking position can be adjusted into satisfactory accuracy.

Keywords: AVP; automated valet parking; priori map; accurate adjustment; automatic charging; path coordination and optimisation strategy; FORLPD control strategy.

DOI: 10.1504/IJVD.2021.122691

International Journal of Vehicle Design, 2021 Vol.87 No.1/2/3/4, pp.120 - 145

Received: 20 Apr 2020
Accepted: 15 Apr 2021
Published online: 05 May 2022 *

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