Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction

• URL: http://arxiv.org/abs/2603.09399v1
• Date: Tue, 10 Mar 2026 09:12:16 GMT
• Title: Vision-Augmented On-Track System Identification for Autonomous Racing via Attention-Based Priors and Iterative Neural Correction
• Abstract summary: This paper proposes a vision-augmented, iterative system identification framework for autonomous vehicles.<n>A lightweight CNN (MobileNetV3) translates visual road textures into a continuous friction prior, providing a robust "warm-start" for parameter optimization.<n>A derivative-free Nelder-Mead algorithm iteratively extracts physically interpretable tire parameters via a hybrid virtual simulation.
• Score: 17.512282011507356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Operating autonomous vehicles at the absolute limits of handling requires precise, real-time identification of highly non-linear tire dynamics. However, traditional online optimization methods suffer from "cold-start" initialization failures and struggle to model high-frequency transient dynamics. To address these bottlenecks, this paper proposes a novel vision-augmented, iterative system identification framework. First, a lightweight CNN (MobileNetV3) translates visual road textures into a continuous heuristic friction prior, providing a robust "warm-start" for parameter optimization. Next, a S4 model captures complex temporal dynamic residuals, circumventing the memory and latency limitations of traditional MLPs and RNNs. Finally, a derivative-free Nelder-Mead algorithm iteratively extracts physically interpretable Pacejka tire parameters via a hybrid virtual simulation. Co-simulation in CarSim demonstrates that the lightweight vision backbone reduces friction estimation error by 76.1 using 85 fewer FLOPs, accelerating cold-start convergence by 71.4. Furthermore, the S4-augmented framework improves parameter extraction accuracy and decreases lateral force RMSE by over 60 by effectively capturing complex vehicle dynamics, demonstrating superior performance compared to conventional neural architectures.

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