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Erschienen in:
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
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.