mlsanityData to 14 Oct 2026

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

arXiv 2507.15025 ↗First posted 20 Jul 2025cs.SE cs.AIcited by 5

Not on the map. Why?

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.

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

42 of 76 references.

References · 42

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  6. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  7. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 1,011
  8. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  9. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  10. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 752
  11. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 309
  12. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
30 more
  1. 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 223
  2. 2024Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?cited by 159
  3. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 126
  4. 2023Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMscited by 115
  5. 2024Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoningcited by 108
  6. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 50
  7. 2025A Survey on Large Language Models with some Insights on their Capabilities and Limitationscited by 42
  8. 2024LangProp: A code optimization framework using Large Language Models applied to drivingcited by 18
  9. 2024Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Plannerscited by 15
  10. 2024Chat2Scenario: Scenario Extraction From Dataset Through Utilization of Large Language Modelcited by 14
  11. 2024Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understandingcited by 11
  12. 2024VeCoGen: Automating Generation of Formally Verified C Code with Large Language Modelscited by 10
  13. 2024LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videoscited by 9
  14. 2024LeGEND: A Top-Down Approach to Scenario Generation of Autonomous Driving Systems Assisted by Large Language Modelscited by 9
  15. 2024Synergy of Large Language Model and Model Driven Engineering for Automated Development of Centralized Vehicular Systemscited by 7
  16. 2024Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineeringcited by 7
  17. 2024Adopting RAG for LLM-Aided Future Vehicle Designcited by 6
  18. 2025Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Studycited by 6
  19. 2024CLUE: Concept-Level Uncertainty Estimation for Large Language Modelscited by 5
  20. 2025LLM-enabled Instance Model Generationcited by 5
  21. 2025LLM-based Iterative Approach to Metamodeling in Automotivecited by 4
  22. 2025Optimizing Retrieval Augmented Generation for Object Constraint Languagecited by 4
  23. 2024Towards Single-System Illusion in Software-Defined Vehicles -- Automated, AI-Powered Workflowcited by 3
  24. 2025On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Softwarecited by 3
  25. 2025Arrow-Guided VLM: Enhancing Flowchart Understanding via Arrow Direction Encodingcited by 3
  26. 2025Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systemscited by 3
  27. 2025Querying Large Automotive Software Models: Agentic vs. Direct LLM Approachescited by 3
  28. 2024Towards Specification-Driven LLM-Based Generation of Embedded Automotive Softwarecited by 2
  29. 2025Are requirements really all you need? A case study of LLM-driven configuration code generation for automotive simulationscited by 2
  30. 2025RECSIP: REpeated Clustering of Scores Improving the Precisioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026Predicting LLM Output Length via Entropy-Guided Representationscited by 9
  2. 2025LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systemscited by 2
  3. 2026LLM-Empowered Functional Safety and Security by Design in Automotive Systemscited by 1
  4. 2026LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domaincited by 0
  5. 2026Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GenAI for Automotive Software Development: From Requirements to Wheels
  2. 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directions
  3. 2025Generative AI for Autonomous Driving: Frontiers and Opportunities
  4. 2025Generative AI for Autonomous Driving: A Review
  5. 2025GenAI-based test case generation and execution in SDV platform
  6. 2025Generative AI in Transportation Planning: A Survey
  7. 2025Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap
  8. 2024Generating a Low-code Complete Workflow via Task Decomposition and RAG