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2021 | OriginalPaper | Buchkapitel

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

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

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

Verlag: 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.
Fußnoten
1
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.
 
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Metadaten
Titel
An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated Driving
verfasst von
Michael Mock
Stephan Scholz
Frédérik Blank
Fabian Hüger
Andreas Rohatschek
Loren Schwarz
Thomas Stauner
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-83906-2_21

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