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arXiv · 2602.15642

Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing

Alexander Wachter·Alexander Willert·Marc-Philip Ecker·Christian Hartl-Nesic
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Abstract

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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Alexander Wachter, Alexander Willert, Marc-Philip Ecker, Christian Hartl-Nesic. 2026-02-17. Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing. https://arxiv.org/abs/2602.15642

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