Automotive safety and machine learning initial results from a study on how to adapt the ISO 26262 safety standard
Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on whi...
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| Vydáno v: | 2018 IEEE ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) s. 47 - 49 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY, USA
ACM
28.05.2018
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| Edice: | ACM Conferences |
| Témata: | |
| ISBN: | 1450357393, 9781450357395, 9781538662618, 1538662612 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on which parts of ISO 26262 represent the most critical gaps between safety engineering and ML development. While this paper only reports the first steps toward a larger research endeavor, we report three adaptations that are critically needed to allow ISO 26262 compliant engineering, and related suggestions on how to evolve the standard. |
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| ISBN: | 1450357393 9781450357395 9781538662618 1538662612 |
| DOI: | 10.1145/3194085.3194090 |