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Machine learning-based fault injection for hazard analysis and risk assessment

Tijdschriftbijdrage - Tijdschriftartikel

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.
Tijdschrift: Lecture notes in computer science
ISSN: 0302-9743
Volume: 128
Pagina's: 178 - 192
Jaar van publicatie:2021
Trefwoorden:A1 Journal article
Toegankelijkheid:Open