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

N. Petrovic, A. C. Knoll · 2026 · Verein Deutscher Ingenieure e.V. eBooks

DOI: 10.51202/9783181024751-39 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

この論文は、ソフトウェア定義車両(SDVs)の端末自動車設計とソフトウェア開発を可能にするための人工知能(AI)の可能性を調査します。Central Car Server(CeCaS)およびその後のEU資金調達プロジェクトにおける私たちの貢献に基づき、私たちは、自動的に自然言語の要件をシステムアーキテクチャ、実行可能なソフトウェア、および検証アーティファクトに変換するために、モデル駆動エンジニアリング(MDE)とLarge Language Models(LLMs)を統合するAI駆動ツールチェーンを紹介します。このアプローチは、追跡性、一貫性、および安全に重要な自動車開発要件の遵守を強調しています。検証は、適応性

Machine translation (Meta M2M100); the English original follows.

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 (CeCaS) and follow-up EU-funded projects, we present an AI-driven toolchain integrating Large Language Models (LLMs) with Model-Driven Engineering (MDE) to automate the transformation of natural-language requirements into system architectures, executable software, and validation artifacts. The approach emphasizes traceability, consistency, and compliance with safety-critical automotive development requirements. Validation is performed on a centralized vehicle computing platform supporting adaptive cruise control and emergency braking, tested in Hardware-in-the-Loop (HiL) and Software-in-the-Loop (SiL) environments. We discuss current capabilities, practical limitations, and future directions toward increasingly automated, reliable, and quality-assured automotive software development with minimal human intervention.

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Topics: Engineering, Software, Computer science, Automotive industry, Software development, Systems engineering

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