Summary of Engineering Safety Requirements For Autonomous Driving with Large Language Models, by Ali Nouri et al.
Engineering Safety Requirements for Autonomous Driving with Large Language Models
by Ali Nouri, Beatriz Cabrero-Daniel, Fredrik Törner, Hȧkan Sivencrona, Christian Berger
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers propose a pipeline using Large Language Models (LLMs) to automatically refine and decompose requirements in the automotive domain, where frequent updates are common. The pipeline takes an item definition as input and outputs safety requirements in the form of solutions. Additionally, it reviews the requirement dataset, identifying redundant or contradictory requirements. The study defines characteristics for assessing LLM capabilities and uses design science with multiple iterations to evaluate each cycle quantitatively and qualitatively. Finally, the prototype is implemented at a case company, where experts assess its efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can help automatically update safety requirements in the automotive domain, which often changes frequently. Researchers created a pipeline that takes an item definition as input and produces safety requirements as output. The pipeline also checks for duplicate or conflicting requirements. To test this idea, experts from different companies reviewed each step to see if it was working well. |
