用轨迹误差反推赛道特性，自动优化赛车路径。
Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing
- 用卡尔曼启发的反馈机制，将跟踪误差转为赛道信息。
- 仿真中比最大静态加速度策略快17.38%，实车提升7.60%。
- 无需调摩擦参数，适配不同轮胎和真实路况。
我们提出一种闭环自主赛车路线优化框架，结合基于NURBS的轨迹表示、CMA-ES全局轨迹优化及控制器引导的空间反馈。不同于将跟踪误差视为瞬时扰动，本方法通过卡尔曼启发的空间更新，将其作为局部赛道特性的信息信号。由此构建自适应的加速度约束图，迭代优化轨迹以实现空间变化的赛道与车辆行为下的近优性能。仿真中相比使用最大静态加速度参数化的控制器，平均单圈时间减少17.38%；在真实硬件上，使用从高摩擦到低摩擦的不同轮胎化合物测试，无需显式参数化摩擦，仍实现7.60%的单圈时间改进，证明了该方法在真实场景中对抓地力变化的鲁棒性。
原文摘要 · Abstract (English)
We present a closed-loop framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback. Instead of treating tracking errors as transient disturbances, our method exploits them as informative signals of local track characteristics via a Kalman-inspired spatial update. This enables the construction of an adaptive, acceleration-based constraint map that iteratively refines trajectories toward near-optimal performance under spatially varying track and vehicle behavior. In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration. On real hardware, tested with different tire compounds ranging from high to low friction, we obtain a 7.60% lap time improvement without explicitly parametrizing friction. This demonstrates robustness to changing grip conditions in real-world scenarios.
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