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Title
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Machine learning-based fault injection for hazard analysis and risk assessment
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Author
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Abstract
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| Current automotive standards such as ISO 26262 require Hazard Analysis and Risk Assessment (HARA) on possible hazards and consequences of safety-critical components. This work attempts to ease this labour-intensive process by using machine learning-based fault injection to discover representative hazardous situations. Using a Simulation-Aided Hazard Analysis and Risk Assessment (SAHARA) methodology, a visualisation and suggested hazard classification is then presented for the safety engineer. We demonstrate this SAHARA methodology using machine learning-based fault injection on a safety-critical use case of an adaptive cruise control system, to show that our approach can discover, visualise, and classify hazardous situations in a (semi-)automated manner in around twenty minutes. |
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Language
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English
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Source (journal)
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Lecture notes in computer science. - Berlin, 1973, currens
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Source (book)
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Computer Safety, Reliability, and Security : 40th International Conference, SAFECOMP 2021, York, UK, September 8–10, 2021
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Source (series)
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Programming and software engineering(LNPSE); 12852
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Publication
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Berlin
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2021
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ISBN
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978-3-030-83902-4
978-3-030-83903-1
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DOI
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10.1007/978-3-030-83903-1_12
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Volume/pages
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12852
(2021)
, p. 178-192
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ISI
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000696703000012
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Full text (Publisher's DOI)
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Full text (open access)
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