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

• URL: http://arxiv.org/abs/2602.15642v1
• Date: Tue, 17 Feb 2026 15:10:44 GMT
• Title: Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing
• Abstract summary: We present a framework for autonomous raceline optimization that combines NURBS-based trajectory representation, CMA-ES global trajectory optimization, and controller-guided spatial feedback.<n>In simulation, our approach achieves a 17.38% lap time reduction compared to a controller parametrized with maximum static acceleration.<n>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.
• Score: 74.83272587893508
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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