Li Jun-Ting, Chen Chih-Keng, Ren Hongbin
Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10604, Taiwan.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2024 May 21;24(11):3281. doi: 10.3390/s24113281.
This article presents a hierarchical control framework for autonomous vehicle trajectory planning and tracking, addressing the challenge of accurately following high-speed, at-limit maneuvers. The proposed time-optimal trajectory planning and tracking (TOTPT) framework utilizes a hierarchical control structure, with an offline trajectory optimization (TRO) module and an online nonlinear model predictive control (NMPC) module. The TRO layer generates minimum-lap-time trajectories using a direct collocation method, which optimizes the vehicle's path, velocity, and control inputs to achieve the fastest possible lap time, while respecting the vehicle dynamics and track constraints. The NMPC layer is responsible for precisely tracking the reference trajectories generated by the TRO in real time. The NMPC also incorporates a preview algorithm that utilizes the predicted future travel distance to estimate the optimal reference speed and curvature for the next time step, thereby improving the overall tracking performance. Simulation results on the Catalunya circuit demonstrated the framework's capability to accurately follow the time-optimal raceline at an average speed of 116 km/h, with a maximum lateral error of 0.32 m. The NMPC module uses an acados solver with a real-time iteration (RTI) scheme, to achieve a millisecond-level computation time, making it possible to implement it in real time in autonomous vehicles.
本文提出了一种用于自动驾驶车辆轨迹规划与跟踪的分层控制框架，以应对精确跟踪高速极限机动的挑战。所提出的时间最优轨迹规划与跟踪（TOTPT）框架采用分层控制结构，包括一个离线轨迹优化（TRO）模块和一个在线非线性模型预测控制（NMPC）模块。TRO层使用直接配置法生成最小单圈时间轨迹，该方法在考虑车辆动力学和赛道约束的同时，优化车辆路径、速度和控制输入，以实现尽可能快的单圈时间。NMPC层负责实时精确跟踪TRO生成的参考轨迹。NMPC还集成了一种预览算法，该算法利用预测的未来行驶距离来估计下一个时间步的最优参考速度和曲率，从而提高整体跟踪性能。在加泰罗尼亚赛道上的仿真结果表明，该框架能够以116公里/小时的平均速度精确跟踪时间最优赛道线，最大横向误差为0.32米。NMPC模块使用带有实时迭代（RTI）方案的acados求解器，以实现毫秒级的计算时间，从而有可能在自动驾驶车辆中实时实现。
