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× improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.
@article{arxiv.2306.06330,
title = {Autonomous Drifting with 3 Minutes of Data via Learned Tire Models},
author = {Franck Djeumou and Jonathan Y. M. Goh and Ufuk Topcu and Avinash Balachandran},
journal= {arXiv preprint arXiv:2306.06330},
year = {2023}
}