Ergo, SMIRK is safe: a safety case for a machine learning component in a pedestrian automatic emergency brake system

Ergo, SMIRK is safe : a safety case for a machine learning component in a pedestrian automatic emergency brake system

Borg, Markus (författare): Lunds universitet,RISE,Lund University, Sweden,Programvarusystem,Institutionen för datavetenskap,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Software Engineering Research Group,Department of Computer Science,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH,Research Institutes of Sweden (RISE)

Socha, Kasper (författare): RISE,Mobilitet och system,Lund University, Sweden,Research Institutes of Sweden (RISE)

Bui, Thanh (författare): RISE,Mobilitet och system,Research Institutes of Sweden (RISE)

Tomaszewski, Piotr (författare): RISE,Mobilitet och system,Research Institutes of Sweden (RISE)

Helali Moghadam, Mahshid (författare): RISE,Research Institutes of Sweden (RISE)

(creator_code:org_t)

2023-03-01
2023
Engelska.
Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; 31:2, s. 335-

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Abstract Ämnesord

Stäng

Integration of machine learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system. This paper reports results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic emergency braking demonstrator running in an industry-grade simulator. We demonstrate an application of AMLAS on SMIRK for a minimalistic operational design domain, i.e., we share a complete safety case for its integrated ML-based component. Finally, we report lessons learned and provide both SMIRK and the safety case under an open-source license for the research community to reuse. © 2023, The Author(s).

Av författaren/redakt...: Borg, Markus; Henriksson, Jens; Socha, Kasper; Lennartsson, Olo ...; Sonnsjö Lönegren ...; Bui, Thanh; visa fler...; Tomaszewski, Pio ...; Sathyamoorthy, S ...; Brink, Sebastian; Helali Moghadam, ...; visa färre...

Om ämnet

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