Virtual parking path planning in narrow roads based on fuzzy pure pursuit algorithm

Qingyi Men, Yongwei Wang, Guangwei Cheng, Ziyang Zhang, Xuefeng Zhu and Hui Zhou

PLOS ONE, 2025, vol. 20, issue 12, 1-15

Abstract: To address the issues of low adaptability and significant tracking errors in parking scenarios when using fixed look-ahead distance Pure Pursuit (PP) algorithms, this paper proposes an automatic parking path tracking control algorithm based on Fuzzy Pure Pursuit (FPP). Considering the influence of road curvature on look-ahead distance, a fuzzy controller is designed to output speed proportionality coefficient and curvature proportionality coefficient. This enables adaptive adjustment of the look-ahead distance according to vehicle speed and road curvature, thereby enhancing path adaptability and tracking accuracy. Prescan/CarSim/Simulink simulation results demonstrate that in vertical parking scenarios, the FPP-based tracking control algorithm outperforms traditional PP algorithms in tracking performance for desired paths and heading angles. The tracking error is reduced by 4.8%, and the heading angle error is reduced by 7.3%. The test results of the Apollo advanced platform show that, under different initial heading angles, the vehicle is able to successfully track the parking path and completes the parking operation without collisions. The tracking control algorithm based on FPP has excellent environmental adaptability.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335911

DOI: 10.1371/journal.pone.0335911

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