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arXiv · 2306.06330

Autonomous Drifting with 3 Minutes of Data via Learned Tire Models

Franck Djeumou·Jonathan Y. M. Goh·Ufuk Topcu·Avinash Balachandran
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Abstract

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data -- less than three minutes -- is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a $4 \times$ improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.

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Franck Djeumou, Jonathan Y. M. Goh, Ufuk Topcu, Avinash Balachandran. 2023-06-10. Autonomous Drifting with 3 Minutes of Data via Learned Tire Models. https://arxiv.org/abs/2306.06330

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