# Neural Network Tire Force Modeling for Automated Drifting

arXiv: https://arxiv.org/abs/2407.13760 · HTML: https://mlsanity.com/paper/2407.13760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Autonomous Drifting with 3 Minutes of Data via Learned Tire Models](https://mlsanity.com/paper/2306.06330/) (2023)

## Built on by (most cited first, 2 of 2)

- [Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies](https://mlsanity.com/paper/2410.20990/) (2024)
- [Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning](https://mlsanity.com/paper/2608.00113/) (2026)

## Abstract (arXiv)

Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network's ability to express previously unmodeled, latent dynamics in the drifting condition.