# Autonomous Drifting with 3 Minutes of Data via Learned Tire Models

arXiv: https://arxiv.org/abs/2306.06330 · HTML: https://mlsanity.com/paper/2306.06330/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 10 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling](https://mlsanity.com/paper/2109.06407/) (2021)
- [Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs](https://mlsanity.com/paper/2201.05715/) (2022)
- [End-to-End Velocity Estimation For Autonomous Racing](https://mlsanity.com/paper/2003.06917/) (2020)
- [Tire Force Estimation in Intelligent Tires Using Machine Learning](https://mlsanity.com/paper/2010.06299/) (2020)

## Built on by (most cited first, 7 of 7)

- [Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies](https://mlsanity.com/paper/2410.20990/) (2024)
- [First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling](https://mlsanity.com/paper/2411.00107/) (2024)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)
- [Neural Network Tire Force Modeling for Automated Drifting](https://mlsanity.com/paper/2407.13760/) (2024)
- [Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits](https://mlsanity.com/paper/2507.23339/) (2025)
- [Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains](https://mlsanity.com/paper/2607.13319/) (2026)
- [Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond](https://mlsanity.com/paper/2609.12660/) (2026)

## Abstract (arXiv)

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.