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Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment
With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variet...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574863/ https://www.ncbi.nlm.nih.gov/pubmed/37837100 http://dx.doi.org/10.3390/s23198271 |
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author | Kim, Inyoung Kang, Donghyo Jeong, Harim Lee, Soomok Yun, Ilsoo |
author_facet | Kim, Inyoung Kang, Donghyo Jeong, Harim Lee, Soomok Yun, Ilsoo |
author_sort | Kim, Inyoung |
collection | PubMed |
description | With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations. |
format | Online Article Text |
id | pubmed-10574863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105748632023-10-14 Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment Kim, Inyoung Kang, Donghyo Jeong, Harim Lee, Soomok Yun, Ilsoo Sensors (Basel) Article With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations. MDPI 2023-10-06 /pmc/articles/PMC10574863/ /pubmed/37837100 http://dx.doi.org/10.3390/s23198271 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Inyoung Kang, Donghyo Jeong, Harim Lee, Soomok Yun, Ilsoo Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title | Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title_full | Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title_fullStr | Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title_full_unstemmed | Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title_short | Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment |
title_sort | method of evaluating multiple scenarios in a single simulation run for automated vehicle assessment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574863/ https://www.ncbi.nlm.nih.gov/pubmed/37837100 http://dx.doi.org/10.3390/s23198271 |
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