# GenAI for Automotive Software Development: From Requirements to Wheels

arXiv: https://arxiv.org/abs/2507.18223 · HTML: https://mlsanity.com/paper/2507.18223/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems](https://mlsanity.com/paper/2404.05508/) (2024)
- [Adopting RAG for LLM-Aided Future Vehicle Design](https://mlsanity.com/paper/2411.09590/) (2024)
- [Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study](https://mlsanity.com/paper/2503.04506/) (2025)
- [LLM-enabled Instance Model Generation](https://mlsanity.com/paper/2503.22587/) (2025)
- [Optimizing Retrieval Augmented Generation for Object Constraint Language](https://mlsanity.com/paper/2505.13129/) (2025)
- [Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulations](https://mlsanity.com/paper/2505.13263/) (2025)

## Built on by (most cited first, 5 of 5)

- [A Unified Perception-Language-Action Framework for Adaptive Autonomous Driving](https://mlsanity.com/paper/2507.23540/) (2025)
- [LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems](https://mlsanity.com/paper/2511.21877/) (2025)
- [LLM-Empowered Functional Safety and Security by Design in Automotive Systems](https://mlsanity.com/paper/2601.02215/) (2026)
- [GenAI-Driven Approach to RISC-V Supply Chain Exploration](https://mlsanity.com/paper/2605.15223/) (2026)
- [LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain](https://mlsanity.com/paper/2607.14659/) (2026)

## Abstract (arXiv)

This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabilities. The process starts with requirements as input, while the main generated outputs are test scenario code for simulation environment, together with implementation of desired ADAS capabilities targeting hardware platform of the vehicle connected to testbench. Moreover, we introduce additional steps for requirements consistency checking leveraging Model-Driven Engineering (MDE). In the proposed workflow, Large Language Models (LLMs) are used for model-based summarization of requirements (Ecore metamodel, XMI model instance and OCL constraint creation), test scenario generation, simulation code (Python) and target platform code generation (C++). Additionally, Retrieval Augmented Generation (RAG) is adopted to enhance test scenario generation from autonomous driving regulations-related documents. Our approach aims shorter compliance and re-engineering cycles, as well as reduced development and testing time when it comes to ADAS-related capabilities.