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An Approach for Deriving Reduced Collision Scenarios for Highly Automated Driving Systems

  • Scenario analysis is essential for the validation of highly automated driving (HAD) systems. The complexity of overall system safety is increasing in terms of Functional Safety (FuSa) and Safety of Intended Functionality (SOTIF). However, field testing of all possible safety-critical scenarios is hardly possible for automated vehicles. Therefore, scenario simulation is necessary for HAD to support the validation process and has gained acceptance in recent years. However, scenariobased analysis leads to an explosion of scenarios, so a scenario database is required at the beginning of the development phase. Hence, scenario reduction approaches need to be integrated in the conceptual phase to reduce the scenario modeling and testing effort. The contribution of this paper is to present simulation-based testing approaches for determining a reduced set of collision scenarios, taking into account the sensitivity of safety-critical parameters. Furthermore, a set of application scenarios is simulated to demonstrate the scenario reductionScenario analysis is essential for the validation of highly automated driving (HAD) systems. The complexity of overall system safety is increasing in terms of Functional Safety (FuSa) and Safety of Intended Functionality (SOTIF). However, field testing of all possible safety-critical scenarios is hardly possible for automated vehicles. Therefore, scenario simulation is necessary for HAD to support the validation process and has gained acceptance in recent years. However, scenariobased analysis leads to an explosion of scenarios, so a scenario database is required at the beginning of the development phase. Hence, scenario reduction approaches need to be integrated in the conceptual phase to reduce the scenario modeling and testing effort. The contribution of this paper is to present simulation-based testing approaches for determining a reduced set of collision scenarios, taking into account the sensitivity of safety-critical parameters. Furthermore, a set of application scenarios is simulated to demonstrate the scenario reduction approaches by considering the detected collisions under a specific or restricted Operational Design Domain (ODD). In addition, this study supports the provision of a safety argument with evidence by introducing a variance-based sensitivity analysis and a scenario database that can be used as an input data set for an artificial intelligence system.show moreshow less

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Metadaten
Author:Marzana KhatunORCiD, Heinrich Litagin, Rolf Jung, Michael GlaßORCiD
DOI:https://doi.org/10.1007/978-3-031-14862-0_13
Identifier:978-3-031-14861-3 OPAC HS OPAC extern
Identifier:978-3-031-14862-0 OPAC HS OPAC extern
Parent Title (English):Computer Safety, Reliability, and Security. SAFECOMP 2022 Workshops: DECSoS, DepDevOps, SASSUR, SENSEI, USDAI, and WAISE Munich, Germany, September 6–9, 2022 Proceedings
Publisher:Springer Nature Switzerland
Place of publication:Cham
Editor:Mario Trapp, Erwin Schoitsch, Jérémie Guiochet, Friedemann Bitsch
Document Type:conference proceeding (article)
Conference:DECSoS, DepDevOps, SASSUR, SENSEI, USDAI, and WAISE Munich, Germany, September 6–9, 2022
Language:English
Date of Publication (online):2022/09/07
Year of first Publication:2022
Tag:CarMaker; Functional Safety; Highly Automated Driving System; Lane Change; Parameter variation
Number of pages:12 Seiten
First Page:166
Last Page:177
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Research focus:FSP2: Mobilität
Eigenständige Forschungsfelder
Publication Lists:Jung, Rolf
Khatun, Marzana
Litagin, Heinrich
Publication reviewed:begutachtet
Release Date:2022/09/12
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