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AI advances in automotive software and vehicle dynamics research

By PulseAugur Editorial · [2 sources] ·

Two recent arXiv papers explore advanced AI applications in distinct fields. The first paper details an AI-driven toolchain that uses large language models and model-driven engineering to automate automotive software development for Software-Defined Vehicles (SDVs), aiming for increased automation and quality assurance. The second paper introduces a novel machine learning technique called Sensitivity-Orthogonal Residual Learning, designed to improve the accuracy of estimating vehicle sideslip and cornering stiffness by decoupling these estimations. AI

IMPACT These papers highlight AI's growing role in specialized domains, from automating complex software engineering tasks to refining vehicle dynamics estimation.

RANK_REASON Two distinct research papers published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI advances in automotive software and vehicle dynamics research

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nenad Petrovic, Alois Knoll ·

    AI-Based End-to-End Automotive Design and Software Development for SDVs

    arXiv:2610.29160v1 Announce Type: cross Abstract: This paper explores the potential of Artificial Intelligence (AI) to enable end-to-end automotive design and software development for Software-Defined Vehicles (SDVs). Based on our contributions within the Central Car Server (CeCa…

  2. arXiv cs.LG TIER_1 English(EN) · Kyoungtae Ji, Dongyeol Won, Kyoungseok Han ·

    Sensitivity-Orthogonal Residual Learning for Adaptive Sideslip and Cornering Stiffness Estimation

    arXiv:2610.27586v1 Announce Type: cross Abstract: Online adaptation of the cornering stiffness keeps a model-based sideslip estimator valid as tires change, but competes with learned model correction for the same filter innovation. A corrector with enough parameters can absorb th…

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