# AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing

arXiv: https://arxiv.org/abs/2607.00141 · HTML: https://mlsanity.com/paper/2607.00141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [Learning-enabled Acceleration of Scenario-based Model Predictive Control](https://mlsanity.com/paper/2607.12775/) (2026)

## Abstract (arXiv)

This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.