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Towards LLM-augmented Situation Space Analysis for the Hazard and Risk Assessment of Automotive Systems

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dc.contributor.authorHillen, Daniel
dc.contributor.authorHelten, Catharina
dc.contributor.authorReich, Jan
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:28Z
dc.date.available2024-10-21T18:24:28Z
dc.date.issued2024
dc.description.abstractThe hazard analysis and risk assessment (HARA) is a fundamental artifact of safety engineering that requires substantial effort from experienced engineers to conform to standards. The cost combined with today’s shortage of trained personnel demands technical solutions to reduce these efforts. Recent advances in generative AI provide new opportunities to support engineers with complex and creative tasks such as the HARA that require knowledge of the system and the environment. However, for now, the results provided by large language models are not reliable enough for safety artifacts so human engineers must review and approve them. In our study, we present a tool to create a HARA based on human-AI cooperation. Our goal is to keep human engineers involved in each decision and guide them by providing arguments, examples, and assessment proposals. In our case study, the engineers benefit from the additional reasonings and proposed assessments of the AI tool.en
dc.identifier.doi10.18420/inf2024_58
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45219
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectOperational Design Domain
dc.subjectChatGPT
dc.subjectGenerative AI
dc.titleTowards LLM-augmented Situation Space Analysis for the Hazard and Risk Assessment of Automotive Systemsen
dc.typeText/Conference Paper
gi.citation.endPage714
gi.citation.publisherPlaceBonn
gi.citation.startPage709
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleGRANITE – EJEA: Europe meets Japan: Intercultural Workshop on Data Sovereignty and Generative AI: Applications, Design, Social, Ethical and Technological Impact

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