Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models
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Abstract (arXiv)
DevOps is a necessity in many industries, including the development of Autonomous Vehicles. In those settings, there are iterative activities that reduce the speed of SafetyOps cycles. One of these activities is "Hazard Analysis & Risk Assessment" (HARA), which is an essential step to start the safety requirements specification. As a potential approach to increase the speed of this step in SafetyOps, we have delved into the capabilities of Large Language Models (LLMs). Our objective is to systematically assess their potential for application in the field of safety engineering. To that end, we propose a framework to support a higher degree of automation of HARA with LLMs. Despite our endeavors to automate as much of the process as possible, expert review remains crucial to ensure the validity and correctness of the analysis results, with necessary modifications made accordingly.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 14 Mar 2024.
Builds on
5 of 17 references.
References · 5
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
- 2023Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPTcited by 13
- 2023ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?cited by 9
- 2020SafetyOpscited by 3
Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 4.
8 papers.
- 2024Engineering Safety Requirements for Autonomous Driving with Large Language Modelscited by 14
- 2024Adopting RAG for LLM-Aided Future Vehicle Designcited by 6
- 2025Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devicescited by 0
- 2025Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systemscited by 0
- 2026From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Drivingcited by 0
- 2026Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPAcited by 0
- 2026Traceable LLM-Generated Hazard Scenarios for Operational Safety Analysis of Aviation Systems Using ASRS Reportscited by 0
- 2026From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devicescited by 0
- 2026From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices12 Aug 2026
- 2026Traceable LLM-Generated Hazard Scenarios for Operational Safety Analysis of Aviation Systems Using ASRS Reports5 Aug 2026
- 2026Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA9 Jul 2026
- 2026From Concept to Capability: Evaluating 3D Gaussian Splatting for Synthetic Scene Editing in Autonomous Driving3 May 2026
- 2025Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systems7 Oct 2025
- 2025Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices18 Jun 2025
- 2024Adopting RAG for LLM-Aided Future Vehicle Design14 Nov 2024
- 2024Engineering Safety Requirements for Autonomous Driving with Large Language Models24 Mar 2024