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Scenario-based Parameter Boundary Reduction Approach for Highly Automated Driving Vehicles

  • Scenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributionsScenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributions with a specific parameter range. Moreover, the proposed approach is applied to an exemplary use case and the outcomes are demonstrated.show moreshow less

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Metadaten
Author:Marzana KhatunORCiD, Heinrich Litagin, Rolf Jung, Michael Glaß
DOI:https://doi.org/10.53375/icmame.2023.112
Identifier:978-625-00-1526-1 OPAC HS OPAC extern
Parent Title (English):ICMAME 2023 : Conference Proceedings
Place of publication:Dubai, UAE
Document Type:conference proceeding (article)
Conference:International Conference on Mechanical, Automotive and Mechatronics Engineering (ICMAME 2023), 29-30 April 2023, Dubai, UAE
Language:English
Date of Publication (online):2023/04/30
Year of first Publication:2023
Tag:Proportional Integral Derivative; Renewable Energy; Statistical Analysis; Swarm Intelligence
Volume:2023
Article Number:112
Number of pages:6 Seiten
First Page:1
Last Page:6
Institutes:IFM - Institut für Fahrerassistenz und vernetzte Mobilität
Open Access:open_access
Research focus:FSP2: Mobilität
Eigenständige Forschungsfelder
Publication Lists:Jung, Rolf
Khatun, Marzana
Litagin, Heinrich
Publication reviewed:begutachtet
Release Date:2023/12/21
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