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2021 | OriginalPaper | Chapter

An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated Driving

Authors : Michael Mock, Stephan Scholz, Frédérik Blank, Fabian Hüger, Andreas Rohatschek, Loren Schwarz, Thomas Stauner

Published in: Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops

Publisher: Springer International Publishing

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Abstract

Developing a stringent safety argumentation for AI-based perception functions requires a complete methodology to systematically organize the complex interplay between specifications, data and training of AI-functions, safety measures and metrics, risk analysis, safety goals and safety requirements. The paper presents the overall approach of the German research project “KI-Absicherung” for developing a stringent safety-argumentation for AI-based perception functions. It is a risk-based approach in which an assurance case is constructed by an evidence-based safety argumentation.

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previous chapter Human-in-the-Loop Learning Methods Toward Safe DL-Based Autonomous Systems: A Review

next chapter Experimental Conformance Evaluation onUBER ATG Safety Case Framework withANSI/UL 4600

The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Energy within the project “KI Absicherung – Safe AI for Automated Driving”. http://www.ki-absicherung-projekt.de.

ISO 26262: Road vehicles – Functional safety. 2d Edition (2018)

ISO PAS 21448: Road vehicles—Safety of the intended functionality (2019)

BS PAS 1883:2020 Operational design domain (ODD) taxonomy for an automated driving system (ADS). Specification, standard by BSI Group, 31 August 2020

ISO TR 4804: Road vehicles—Safety and cybersecurity for automated driving systems—Design, verification and validation (2020)

ANSI/UL 4600: Standard for Evaluation of Autonomous Products (2020)

Sämann, T., Schlicht, P., Hüger, F.: Strategy to increase the safety of a DNN-based perception for HAD systems. arXiv preprint arXiv:2002.08935 (2020)

Willers, O., Sudholt, S., Raafatnia, S., Abrecht, S.: Safety concerns and mitigation approaches regarding the use of deep learning in safety-critical perception tasks. In: Casimiro, A., Ortmeier, F., Schoitsch, E., Bitsch, F., Ferreira, P. (eds.) SAFECOMP 2020. LNCS, vol. 12235, pp. 336–350. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55583-2_25CrossRef

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Schwalbe, G., et al.: Structuring the safety argumentation for deep neural network based perception in automotive applications. In: Casimiro, A., Ortmeier, F., Schoitsch, E., Bitsch, F., Ferreira, P. (eds.) SAFECOMP 2020. LNCS, vol. 12235, pp. 383–394. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-55583-2_29CrossRef

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Burton, S., Hawkins, R.: Assuring the safety of highly automated driving: state-of-the-art and research perspective, research report April 2020. https://www.york.ac.uk/assuring-autonomy/news/news/report-launch-safety-automated-driving/. Accessed 14 May 2021

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Title: An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated Driving
Authors: Michael Mock
Stephan Scholz
Frédérik Blank
Fabian Hüger
Andreas Rohatschek
Loren Schwarz
Thomas Stauner
Publisher: Springer International Publishing
Book: Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops
Print ISBN: 978-3-030-83905-5

Electronic ISBN: 978-3-030-83906-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-83906-2_21

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