实时调整赛车速度以应对路面抓地力变化，提升动态适应能力。
Online Velocity Profile Generation and Tracking for Sampling-Based Local Planning Algorithms in Autonomous Racing Environments
- 结合前后向求解器与三维赛道采样策略优化速度曲线。
- 在真实竞速场景中验证了高效性与鲁棒性，计算延迟低。
- 适合追求高速稳定控制的自动驾驶赛车系统使用。
本文提出一种面向自动驾驶赛车的在线速度规划方法，可自适应动态约束变化，如轮胎温度导致的抓地力波动及橡胶堆积。该方法融合前向-后向求解器实现速度在线优化，并采用新型空间采样策略进行局部轨迹规划，基于三维赛道表示。生成的速度剖面作为局部规划器的参考，确保对环境与车辆动力学的动态适应性。我们在竞速场景中验证了该方法的鲁棒性能与计算效率，但其对预设赛车线的偏离敏感，且速度剖面存在较高加加速度（jerk）特征。
原文摘要 · Abstract (English)
This work presents an online velocity planner for autonomous racing that adapts to changing dynamic constraints, such as grip variations from tire temperature changes and rubber accumulation. The method combines a forward-backward solver for online velocity optimization with a novel spatial sampling strategy for local trajectory planning, utilizing a three-dimensional track representation. The computed velocity profile serves as a reference for the local planner, ensuring adaptability to environmental and vehicle dynamics. We demonstrate the approach's robust performance and computational efficiency in racing scenarios and discuss its limitations, including sensitivity to deviations from the predefined racing line and high jerk characteristics of the velocity profile.
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