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Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodol...

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Detalles Bibliográficos
Autores principales: Park, Sangmin, Park, Sungho, Jeong, Harim, Yun, Ilsoo, So, Jaehyun (Jason)
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537130/
https://www.ncbi.nlm.nih.gov/pubmed/34696142
http://dx.doi.org/10.3390/s21206929
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author Park, Sangmin
Park, Sungho
Jeong, Harim
Yun, Ilsoo
So, Jaehyun (Jason)
author_facet Park, Sangmin
Park, Sungho
Jeong, Harim
Yun, Ilsoo
So, Jaehyun (Jason)
author_sort Park, Sangmin
collection PubMed
description As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.
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spelling pubmed-85371302021-10-24 Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process Park, Sangmin Park, Sungho Jeong, Harim Yun, Ilsoo So, Jaehyun (Jason) Sensors (Basel) Article As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively. MDPI 2021-10-19 /pmc/articles/PMC8537130/ /pubmed/34696142 http://dx.doi.org/10.3390/s21206929 Text en © 2021 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
Park, Sangmin
Park, Sungho
Jeong, Harim
Yun, Ilsoo
So, Jaehyun (Jason)
Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title_full Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title_fullStr Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title_full_unstemmed Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title_short Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process
title_sort scenario-mining for level 4 automated vehicle safety assessment from real accident situations in urban areas using a natural language process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8537130/
https://www.ncbi.nlm.nih.gov/pubmed/34696142
http://dx.doi.org/10.3390/s21206929
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