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dc.contributor.authorVasudevan, Vinod
dc.contributor.authorAbdullatif, Amr R.A.
dc.contributor.authorKabir, Sohag
dc.contributor.authorCampean, I. Felician
dc.date.accessioned2021-12-10T17:35:25Z
dc.date.accessioned2021-12-22T14:34:03Z
dc.date.available2021-12-10T17:35:25Z
dc.date.available2021-12-22T14:34:03Z
dc.date.issued2022
dc.identifier.citationVasudevan V, Abdullatif ARA, Kabir S et al (2022) A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262. In: Jansen T, Jensen R, Mac Parthaláin N et al (Eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing. Vol 1409: 508-518. Springer, Cham.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18707
dc.descriptionYesen_US
dc.description.abstractAssuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition, lane keep assist etc. The application of ML is making a significant contributions in the automotive industry, it introduces concerns related to the safety and security of these systems. ML models should be robust and reliable throughout and prove their trustworthiness in all use cases associated with vehicle operation. Proving confidence in the safety and security of ML-based systems and there by giving assurance to regulators, the certification authorities, and other stakeholders is an important task. This paper proposes a framework to handle uncertainties of ML model to improve the safety level and thereby certify the ML Models in the automotive industry.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/978-3-030-87094-2_45en_US
dc.rights(c) 2022 Springer Cham. Full-text reproduced in accordance with the publisher's self-archiving policy.
dc.subjectArtificial intelligenceen_US
dc.subjectUncertaintyen_US
dc.subjectRobustnessen_US
dc.subjectCertificationen_US
dc.subjectMachine learningen_US
dc.subjectEvidential deep learningen_US
dc.titleA Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262en_US
dc.status.refereedYesen_US
dc.date.Accepted2021-09-10
dc.date.application2021-11-18
dc.typeBook chapteren_US
dc.date.EndofEmbargo2023-11-18
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full-text of this book chapter will be released for public view at the end of the publisher embargo on 18 Nov 2023.
dc.date.updated2021-12-10T17:35:27Z
refterms.dateFOA2021-12-22T14:34:33Z
dc.openaccess.statusopenAccessen_US


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