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  4. An Integrated Approach to a Safety Argumentation for AI-Based Perception Functions in Automated Driving
 
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August 25, 2021
Conference Paper
Title

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

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.
Author(s)
Mock, Michael  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Scholz, Stefan
Volkswagen AG, Wolfsburg  
Blank, Frederik
Robert-Bosch-GmbH, Stuttgart  
Hüger, Fabian
Volkswagen AG, Wolfsburg  
Rohatschek, Andreas
Robert-Bosch-GmbH, Stuttgart  
Schwarz, Loren
Bayerische Motoren Werke AG -BMW-, München  
Stauner, Thomas
Bayerische Motoren Werke AG -BMW-, München  
Mainwork
Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops, DECSoS, MAPSOD, DepDevOps, USDAI, and WAISE. Proceedings  
Project(s)
KI-Absicherung
Funder
Bundesministerium für Wirtschaft und Klimaschutz
Conference
International Conference on Computer Safety, Reliability and Security (SAFECOMP) 2021  
International Workshop on Artificial Intelligence Safety Engineering (WAISE) 2021  
DOI
10.1007/978-3-030-83906-2_21
Language
Englisch
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • Automated Driving

  • Safe AI

  • Safety argumentation

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