# A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles

arXiv: https://arxiv.org/abs/2304.12263 · HTML: https://mlsanity.com/paper/2304.12263/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Computational Teaching for Driving via Multi-Task Imitation Learning](https://mlsanity.com/paper/2410.01608/) (2024)
- [A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential](https://mlsanity.com/paper/2312.14808/) (2023)
- [TurboMPC: Fast, Scalable, and Differentiable Model Predictive Control on the GPU](https://mlsanity.com/paper/2606.24039/) (2026)
- [3D Lane Detection with Odometry for High-Speed Vehicle Racing](https://mlsanity.com/paper/2607.14248/) (2026)

## Abstract (arXiv)

The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including robustly contending with tire force saturation, balancing model fidelity and computational efficiency, and coordinating inputs with the lower level chassis control system. This work studies Nonlinear Model Predictive Control for limit handling, specifically adapting to changing tire-road conditions and maximally allocating tire force utilization. We present a novel hierarchical framework that combines a single-track model with longitudinal weight transfer dynamics in the predictive control layer, with lateral brake distribution occurring at the chassis control layer. This vehicle model is simultaneously used in an Unscented Kalman Filter for online friction estimation. Comparative experiments on a full-scale vehicle operating on a race track at up to 95% of maximum tire force usage demonstrate the overall practical effectiveness of this approach.