Control variables and universes of discourse.

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.针对采用固定预瞄距离纯追踪（Pure Pursuit, PP）算法时，泊车场景下适配性不足、追踪误差偏大的问题，本文提出一种基于模糊纯追踪（Fuzzy Pure Pursuit, FPP）的自动泊车路径追踪控制算法。考虑到道路曲率对预瞄距离的影响，本文设计了一款模糊控制器，用于输出车速比例系数与曲率比例系数，从而可根据车辆车速与道路曲率自适应调整预瞄距离，进而提升路径适配性与追踪精度。Prescan/CarSim/Simulink仿真结果表明，在垂直泊车场景中，基于FPP的追踪控制算法在期望路径与航向角的追踪性能上优于传统PP算法，其追踪误差降低4.8%，航向角误差降低7.3%。阿波罗（Apollo）高级平台的测试结果显示，在不同初始航向角条件下，车辆可成功追踪泊车路径并完成泊车操作，且未发生碰撞。基于FPP的追踪控制算法具备优异的环境适配能力。