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A Systematic Approach of Reduced Scenario-based Safety Analysis for Highly Automated Driving Function

  • This paper investigates the scenario catalog generation and scenario reduction approaches for a complete Highly Automated Driving Function (HADF). Such approaches focus on the clustering and/or grouping of scenarios by applying a simple stochastic process at an early stage of development. Dealing with an enormous number of scenarios considering Functional Safety (FuSa), Safety Of The Intended Functionality (SOTIF)including cybersecurity desires intelligent approaches for HADF’s scenario reduction. The reduction of scenarios in HADF is a challenge for automotive researchers since it relates to a large number of parameters (like environmental aspects). The main contributions of the scenario generation and reduction approach proposed in this work are the following: (1) contribution to a complete scenario catalog for a dedicated HADF, (2) logical scenario optimization with parameter distribution, and (3) optimize discretization step for finding semiconcrete scenarios that can be executed. Furthermore, the optimization method incorporatingThis paper investigates the scenario catalog generation and scenario reduction approaches for a complete Highly Automated Driving Function (HADF). Such approaches focus on the clustering and/or grouping of scenarios by applying a simple stochastic process at an early stage of development. Dealing with an enormous number of scenarios considering Functional Safety (FuSa), Safety Of The Intended Functionality (SOTIF)including cybersecurity desires intelligent approaches for HADF’s scenario reduction. The reduction of scenarios in HADF is a challenge for automotive researchers since it relates to a large number of parameters (like environmental aspects). The main contributions of the scenario generation and reduction approach proposed in this work are the following: (1) contribution to a complete scenario catalog for a dedicated HADF, (2) logical scenario optimization with parameter distribution, and (3) optimize discretization step for finding semiconcrete scenarios that can be executed. Furthermore, the optimization method incorporating the Monte-Carlo(MC) experiment with the CarMaker simulation yields a systematic approach to modeling reduced scenarios without redundancy to support safety.show moreshow less

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Metadaten
Author:Marzana KhatunORCiD, Michael GlaßORCiD, Rolf Jung
DOI:https://doi.org/10.5220/0010397403010308
Identifier:978-989-758-513-5 OPAC HS OPAC extern
Identifier:2184-495X OPAC HS OPAC extern
Parent Title (English):Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021)
Publisher:Scitepress
Editor:Karsten Berns, Markus Helfert, Oleg Gusikhin
Document Type:conference proceeding (article)
Conference:VEHITS 2021, 28 - 30. April 2021, web-based event
Language:English
Date of Publication (online):2021/05/07
Year of first Publication:2021
Volume:Vol. 1
Number of pages:8 Seiten
First Page:301
Last Page:308
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Research focus:FSP2: Mobilität
Publication Lists:Jung, Rolf
Khatun, Marzana
Publication reviewed:begutachtet
Release Date:2021/05/12
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