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

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
Blank, Frederik
Bosch AG
Hüger, Fabian
Volkswagen AG
Rohatschek, Andreas
Bosch AG
Schwarz, Loren
BMW Group
Stauner, Thomas
BMW Group
Hauptwerk
Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops, DECSoS, MAPSOD, DepDevOps, USDAI, and WAISE. Proceedings
Project(s)
KI-Absicherung
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)
Konferenz
International Conference on Computer Safety, Reliability and Security (SAFECOMP) 2021
International Workshop on Artificial Intelligence Safety Engineering (WAISE) 2021
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DOI
10.1007/978-3-030-83906-2_21
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Tags
  • AI safety

  • Autononous Driving

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