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Extending the function failure modes taxonomy for intelligent systems with embedded AI components

Published online by Cambridge University Press:  16 May 2024

Felician Campean ,
Unal Yildirim ,
Aleksandr Korsunovs  and
Aleksandr Doikin
Felician Campean*
Affiliation:
University of Bradford, United Kingdom
Unal Yildirim
Affiliation:
Hubei Key Laboratory of Automotive Power Train and Electronic Control, China
Aleksandr Korsunovs
Affiliation:
University of Bradford, United Kingdom
Aleksandr Doikin
Affiliation:
University of Bradford, United Kingdom
*
F.Campean@Bradford.ac.uk

Abstract

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Early consideration of failure modes in the feature development process is essential to identify and trace risks across the physical and embedded AI components of intelligent systems, to enhance the robustness of the feature delivery as well as trust in the AI. This paper introduces an extension of the AIAG/VDA function failure modes taxonomy, to facilitate the integrated analysis of complex intelligent systems with embedded AI. A case study of an autonomous driving feature is discussed as validation of the proposed taxonomy.

Keywords

robust designsystems engineering (SE)autonomous systemsembedded AIfailure mode and effect analysis (FMEA)

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1949 - 1958
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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