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

• URL: http://arxiv.org/abs/2607.00141v1
• Date: Tue, 30 Jun 2026 20:19:35 GMT
• Title: AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing
• Abstract summary: Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC) is a framework for autonomous racing.<n>For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme.<n>For objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner.
• Score: 2.922583645818756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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