Existing approaches to trajectory planning for autonomous racing employ sampling-based methods, generating numerous jerk-optimal trajectories and selecting the most favorable feasible trajectory based on a cost function penalizing deviations from an offline-calculated racing line. While successful on oval tracks, these methods face limitations on complex circuits due to the simplistic geometry of jerk-optimal edges failing to capture the complexity of the racing line. Additionally, they only consider two-dimensional tracks, potentially neglecting or surpassing the actual dynamic potential. In this paper, we present a sampling-based local trajectory planning approach for autonomous racing that can maintain the lap time of the racing line even on complex race tracks and consider the race track's three-dimensional effects. In simulative experiments, we demonstrate that our approach achieves lower lap times and improved utilization of dynamic limits compared to existing approaches. We also investigate the impact of online racing line generation, in which the time-optimal solution is planned from the current vehicle state for a limited spatial horizon, in contrast to a closed racing line calculated offline. We show that combining the sampling-based planner with the online racing line generation can significantly reduce lap times in multi-vehicle scenarios.

翻译：现有自动驾驶赛车的轨迹规划方法采用基于采样的方式，生成大量加加速度最优轨迹，并根据惩罚偏离离线计算赛车线的代价函数选择最有利的可行轨迹。虽然这类方法在椭圆赛道中表现良好，但由于加加速度最优轨迹的简化几何结构无法捕捉赛车线的复杂性，因此在复杂赛道上存在局限性。此外，它们仅考虑二维赛道，可能忽略或超出实际的动力学潜力。本文提出一种基于采样的自动驾驶赛车局部轨迹规划方法，即使在复杂赛道上也能保持赛车线的单圈时间，并充分考虑赛道的三维效应。仿真实验表明，与现有方法相比，本方法实现了更低的单圈时间和更优的动力学极限利用率。我们还研究了在线赛车线生成的影响——即从当前车辆状态出发，在有限空间范围内规划时间最优解（区别于离线计算的封闭赛车线）。结果表明，将基于采样的规划器与在线赛车线生成相结合，可在多车场景下显著降低单圈时间。