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

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MARC

Borg, MarkusLunds 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) (author)

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

Article/chapterEnglish2023

Publisher, publication year, extent ...

2023-03-01
Springer,2023
printrdacarrier

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LIBRIS-ID:oai:DiVA.org:ri-64234
https://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-64234URI
https://doi.org/10.1007/s11219-022-09613-1DOI
https://lup.lub.lu.se/record/c6413675-622a-4634-919c-ee349cb346f4URI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

Open access funding provided by RISE Research Institutes of Sweden. This work was carried out within the SMILE III project financed by Vinnova, FFI, Fordonsstrategisk forskning och innovation under the grant number 2019-05871 and partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation.
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).

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Programvaruteknik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Software Engineering hsv//eng
Automotive demonstrator
Machine learning safety
Safety case
Safety standards
Automotive demonstrator
Machine learning safety
Safety case
Safety standards

Added entries (persons, corporate bodies, meetings, titles ...)

Henriksson, JensSemcon AB, Sweden (author)
Socha, KasperRISE,Mobilitet och system,Lund University, Sweden,Research Institutes of Sweden (RISE) (author)
Lennartsson, OlofInfotiv AB, Sweden (author)
Sonnsjö Lönegren, EliasInfotiv AB, Sweden (author)
Bui, ThanhRISE,Mobilitet och system,Research Institutes of Sweden (RISE)(Swepub:ri)thanhbu@ri.se (author)
Tomaszewski, PiotrRISE,Mobilitet och system,Research Institutes of Sweden (RISE)(Swepub:ri)piotrto@ri.se (author)
Sathyamoorthy, S RQRTECH AB, Sweden (author)
Brink, SebastianCombitech AB, Sweden (author)
Helali Moghadam, MahshidRISE,Research Institutes of Sweden (RISE)(Swepub:ri)mahshid.helali.moghadam@ri.se (author)
RISELund University, Sweden (creator_code:org_t)

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In:Software quality journal: Springer31:2, s. 335-0963-93141573-1367

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