# Survey of GenAI for Automotive Software Development: From Requirements to Executable Code

arXiv: https://arxiv.org/abs/2507.15025 · HTML: https://mlsanity.com/paper/2507.15025/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 76 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 30 of 42)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://mlsanity.com/paper/2309.13007/) (2023)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://mlsanity.com/paper/2402.18272/) (2024)
- [Vision Language Models in Autonomous Driving: A Survey and Outlook](https://mlsanity.com/paper/2310.14414/) (2023)
- [Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs](https://mlsanity.com/paper/2305.11860/) (2023)
- [Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning](https://mlsanity.com/paper/2401.10480/) (2024)
- [Internal Consistency and Self-Feedback in Large Language Models: A Survey](https://mlsanity.com/paper/2407.14507/) (2024)
- [A Survey on Large Language Models with some Insights on their Capabilities and Limitations](https://mlsanity.com/paper/2501.04040/) (2025)
- [LangProp: A code optimization framework using Large Language Models applied to driving](https://mlsanity.com/paper/2401.10314/) (2024)
- [Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners](https://mlsanity.com/paper/2406.00430/) (2024)
- [Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Model](https://mlsanity.com/paper/2404.16147/) (2024)
- [Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding](https://mlsanity.com/paper/2412.16420/) (2024)
- [VeCoGen: Automating Generation of Formally Verified C Code with Large Language Models](https://mlsanity.com/paper/2411.19275/) (2024)
- [LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos](https://mlsanity.com/paper/2406.10857/) (2024)
- [LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Models](https://mlsanity.com/paper/2409.10066/) (2024)
- [Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systems](https://mlsanity.com/paper/2404.05508/) (2024)
- [Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering](https://mlsanity.com/paper/2410.12475/) (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)

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

- [Predicting LLM Output Length via Entropy-Guided Representations](https://mlsanity.com/paper/2602.11812/) (2026)
- [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)
- [LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain](https://mlsanity.com/paper/2607.14659/) (2026)
- [Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification](https://mlsanity.com/paper/2607.16868/) (2026)

## Abstract (arXiv)

Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for handling complex underlying processes. Automotive software development is considered to be a significant area for GenAI adoption, taking into account lengthy and expensive procedures, resulting from the amount of requirements and strict standardization. In this paper, we explore the adoption of GenAI for various steps of automotive software development, mainly focusing on requirements handling, compliance aspects and code generation. Three GenAI-related technologies are covered within the state-of-art: Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Vision Language Models (VLMs), as well as overview of adopted prompting techniques in case of code generation. Additionally, we also derive a generalized GenAI-aided automotive software development workflow based on our findings from this literature review. Finally, we include a summary of a survey outcome, which was conducted among our automotive industry partners regarding the type of GenAI tools used for their daily work activities.