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Near the limits of adhesion, the forces generated by a tire are nonlinear andintricately coupled. Efficient and accurate modelling in this region couldimprove safety, especially in emergency situations where high forces arerequired. To this end, we propose a novel family of tire force models based onneural ordinary differential equations and a neural-ExpTanh parameterization.These models are designed to satisfy physically insightful assumptions whilealso having sufficient fidelity to capture higher-order effects directly fromvehicle state measurements. They are used as drop-in replacements for ananalytical brush tire model in an existing nonlinear model predictive controlframework. Experiments with a customized Toyota Supra show that scarce amountsof driving data -- less than three minutes -- is sufficient to achievehigh-performance autonomous drifting on various trajectories with speeds up to45mph. Comparisons with the benchmark model show a $4 \times$ improvement intracking performance, smoother control inputs, and faster and more consistentcomputation time.

Autonomous Drifting with 3 Minutes of Data via Learned Tire Models