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Published in:
2019 | OriginalPaper | Chapter
Architecture and independence controller for deep learning in safety critical applications
Author : Ulrich Bodenhausen
Published in: 19. Internationales Stuttgarter Symposium
Publisher: Springer Fachmedien Wiesbaden
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The market potential of safety critical products using AI is very attractive and Deep Learning Neural Networks (NN) have proven strengths to provide important functionality. This paper describes some of the challenges in arguing safety of systems using Deep Learning NN, especially functional improvement in context of SOTIF (Safety of the Intended Functionality) or other approaches to provide the safety case. An architecture and independence controller is proposed which can be used beneficially to reduce residual risk of functional insufficiencies for Deep Learning NN based systems.