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Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering

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arxiv 2410.12475 v2 pith:GJ5NXPUU submitted 2024-10-16 cs.MA

Lu Shi , Bin Qi , Jiarui Luo , Yang Zhang , Zhanzhao Liang , Zhaowei Gao , Wenke Deng , Lin Sun This is my paper · ORCID
classification cs.MA
keywords functionalsafetyaegisengineeringadvancedautomotivetaskscomplex
verification ladder T0 review T1 audit T2 compute T3 formal
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Functional safety is a critical aspect of automotive engineering, encompassing all phases of a vehicle's lifecycle, including design, development, production, operation, and decommissioning. This domain involves highly knowledge-intensive tasks. This paper introduces Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. Aegis is specifically designed to support complex functional safety tasks within the automotive sector. It is tailored to perform Hazard Analysis and Risk Assessment(HARA), document Functional Safety Requirements(FSR), and plan test cases for Automatic Emergency Braking(AEB) systems. The most advanced version, Aegis-Max, leverages Retrieval-Augmented Generation(RAG) and reflective mechanisms to enhance its capability in managing complex, knowledge-intensive tasks. Additionally, targeted prompt refinement by professional functional safety practitioners can significantly optimize Aegis's performance in the functional safety domain. This paper demonstrates the potential of Aegis to improve the efficiency and effectiveness of functional safety processes in automotive engineering.

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

    cs.SE 2025-07 conditional novelty 3.0 of 10

    A review of roughly 60 papers and 9 industry respondents finds GPT-family models dominate automotive code generation while requirements handling lags due to confidentiality constraints.

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