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.

翻译：本文提出了一种用于自主赛车线路优化的闭环框架，该框架结合了基于NURBS的轨迹表示、CMA-ES全局轨迹优化以及控制器引导的空间反馈。不同于将跟踪误差视为瞬态扰动，我们的方法通过一种受卡尔曼滤波器启发的空间更新机制，将其作为局部赛道特征的信息信号加以利用。这使得我们能够构建一个基于加速度的自适应约束图，在空间变化的赛道和车辆行为下，迭代地优化轨迹以实现接近最优的性能。在仿真中，与使用最大静态加速度参数化的控制器相比，我们的方法实现了17.38%的单圈时间缩减。在真实硬件上，使用从高到低不同摩擦系数的轮胎配方进行测试，我们在未显式参数化摩擦系数的情况下获得了7.60%的单圈时间提升。这证明了该方法在真实场景中对变化抓地力条件的鲁棒性。