Generative AI in Automotive Engineering: ChatGPT as a Design and Development Copilot

Abstract

Generative AI models like ChatGPT are catalyzing significant shifts within automotive engineering, impacting areas from design and software development to quality assurance, customer service, and training. This narrative literature review synthesizes recent studies and industry examples to evaluate these impacts comprehensively. ChatGPT assists in automating complex documentation tasks, improving compliance with regulatory standards, enhancing real-time simulation workflows, and optimizing after-sales support. Advanced applications highlighted include compliance-aware documentation tools that ensure adherence to ISO 26262 and ASPICE standards, simulation-integrated design agents that accelerate prototype iterations, and retrieval-augmented generation (RAG)-based copilot tools that support engineers with contextually relevant knowledge. However, the deployment of such AI systems introduces technical risks, notably hallucination—where the model produces incorrect or fabricated information—alongside cybersecurity vulnerabilities and potential misuse scenarios. To mitigate these risks and fully leverage the technology's potential, structured expert oversight and rigorous governance frameworks are imperative. This review argues that, with appropriate controls, generative AI can serve as a transformative catalyst for innovation, safety, and efficiency in automotive engineering.

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