mlsanityData to 7 Oct 2026

Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models

arXiv 2403.09565 ↗First posted 14 Mar 2024cs.SE cs.AIcited by 8

Not on the map. Why?

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

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  2. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  3. 2023Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPTcited by 13
  4. 2023ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?cited by 9
  5. 2020SafetyOpscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 4.

8 papers.