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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2602.15642 |
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| _version_ | 1866911452025061376 |
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| author | Wachter, Alexander Willert, Alexander Ecker, Marc-Philip Hartl-Nesic, Christian |
| author_facet | Wachter, Alexander Willert, Alexander Ecker, Marc-Philip Hartl-Nesic, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15642 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing Wachter, Alexander Willert, Alexander Ecker, Marc-Philip Hartl-Nesic, Christian Robotics 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. |
| title | Spatially-Aware Adaptive Trajectory Optimization with Controller-Guided Feedback for Autonomous Racing |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.15642 |