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Artificial Intelligence for Safety-Critical Systems in Industrial and Transportation Domains: A Survey
Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Spain.ORCID iD: 0000-0001-6389-648X
Barcelona Supercomputing Center (BSC), Spain.ORCID iD: 0000-0001-7951-4028
RISE Research Institutes of Sweden AB, Sweden.ORCID iD: 0000-0001-7879-4371
Exida, Italy.ORCID iD: 0009-0002-5102-3205
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2024 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, no 7, article id 176Article in journal (Refereed) Published
Abstract [en]

Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2024. Vol. 56, no 7, article id 176
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Embedded Systems Computer Systems
Research subject
Robotics and Artificial Intelligence
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URN: urn:nbn:se:ltu:diva-105483DOI: 10.1145/3626314Scopus ID: 2-s2.0-85191063705OAI: oai:DiVA.org:ltu-105483DiVA, id: diva2:1857981
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Full text license: CC BY

Available from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-05-15

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Nikolakopoulos, George

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