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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.08489 |
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| _version_ | 1866910203367129088 |
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| author | Arrafi, Musabbir Ahmed Ali, Malik Stiffler, Nicholas M. Kidambi, Krishna Bhavithavya |
| author_facet | Arrafi, Musabbir Ahmed Ali, Malik Stiffler, Nicholas M. Kidambi, Krishna Bhavithavya |
| contents | Accurate modeling of nonlinear vehicle dynamics is essential for high-speed autonomous racing, where controllers operate at the handling limits. Model-based methods are interpretable but rely on simplifying assumptions, while purely learned models capture nonlinearities yet often lack physical consistency and generalization. We propose LE-PAVD (Learning-Enhanced Physics-Aware Vehicle Dynamics), a hybrid model that integrates physics priors with learned components. Our architecture adds four components: load-sensitive Pacejka tire forces, longitudinal load transfer, lateral tire-force effects, and rate-limited actuator inputs. Trained end-to-end on simulation and real-world telemetry, LE-PAVD enforces physical consistency while improving state prediction accuracy. On an unseen track, LE-PAVD reduces average displacement error (ADE) by 16.1$\%$, final displacement error (FDE) by 20.6$\%$, and lowers yaw-rate root mean squared error (RMSE) by 91.3$\%$ versus a deep dynamics baseline, while using 21.6$\%$ fewer FLOPs and achieving approximately 1.50$\times$ faster inference. In closed-loop simulations, LE-PAVD consistently outperforms the baseline by achieving faster lap times by 17.4$\%$ on a training track and 9.5$\%$ on a test track, without any track boundary violations. Overall, LE-PAVD offers a compact, physics-grounded dynamics backbone that improves predictive fidelity and closed-loop performance while reducing inference cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08489 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation Arrafi, Musabbir Ahmed Ali, Malik Stiffler, Nicholas M. Kidambi, Krishna Bhavithavya Robotics Accurate modeling of nonlinear vehicle dynamics is essential for high-speed autonomous racing, where controllers operate at the handling limits. Model-based methods are interpretable but rely on simplifying assumptions, while purely learned models capture nonlinearities yet often lack physical consistency and generalization. We propose LE-PAVD (Learning-Enhanced Physics-Aware Vehicle Dynamics), a hybrid model that integrates physics priors with learned components. Our architecture adds four components: load-sensitive Pacejka tire forces, longitudinal load transfer, lateral tire-force effects, and rate-limited actuator inputs. Trained end-to-end on simulation and real-world telemetry, LE-PAVD enforces physical consistency while improving state prediction accuracy. On an unseen track, LE-PAVD reduces average displacement error (ADE) by 16.1$\%$, final displacement error (FDE) by 20.6$\%$, and lowers yaw-rate root mean squared error (RMSE) by 91.3$\%$ versus a deep dynamics baseline, while using 21.6$\%$ fewer FLOPs and achieving approximately 1.50$\times$ faster inference. In closed-loop simulations, LE-PAVD consistently outperforms the baseline by achieving faster lap times by 17.4$\%$ on a training track and 9.5$\%$ on a test track, without any track boundary violations. Overall, LE-PAVD offers a compact, physics-grounded dynamics backbone that improves predictive fidelity and closed-loop performance while reducing inference cost. |
| title | LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.08489 |