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Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles
The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field te...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606858/ https://www.ncbi.nlm.nih.gov/pubmed/36298087 http://dx.doi.org/10.3390/s22207735 |
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author | Zhu, Yu Wang, Jian Meng, Fanqiang Liu, Tongtao |
author_facet | Zhu, Yu Wang, Jian Meng, Fanqiang Liu, Tongtao |
author_sort | Zhu, Yu |
collection | PubMed |
description | The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized. |
format | Online Article Text |
id | pubmed-9606858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96068582022-10-28 Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles Zhu, Yu Wang, Jian Meng, Fanqiang Liu, Tongtao Sensors (Basel) Review The advancement of autonomous driving technology has had a significant impact on both transportation networks and people’s lives. Connected and automated vehicles as well as the surrounding driving environment are increasingly exchanging information. The traditional open road test or closed field test, which has large costs, lengthy durations, and few diverse test scenarios, cannot satisfy the autonomous driving system’s need for reliable and safe testing. Functional testing is the emphasis of the test since features such as frontal collision and traffic sign warning influence driving safety. As a result, simulation testing will undoubtedly emerge as a new technique for unmanned vehicle testing. A crucial aspect of simulation testing is the creation of test scenarios. With an emphasis on the map generating method and the dynamic scenario production method in the test scenarios, this article explains many scenarios and scenario construction techniques utilized in the process of self-driving car testing. A thorough analysis of the state of relevant research is conducted, and approaches for creating common scenarios as well as brand-new methods based on machine learning are emphasized. MDPI 2022-10-12 /pmc/articles/PMC9606858/ /pubmed/36298087 http://dx.doi.org/10.3390/s22207735 Text en © 2022 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 | Review Zhu, Yu Wang, Jian Meng, Fanqiang Liu, Tongtao Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title | Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title_full | Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title_fullStr | Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title_full_unstemmed | Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title_short | Review on Functional Testing Scenario Library Generation for Connected and Automated Vehicles |
title_sort | review on functional testing scenario library generation for connected and automated vehicles |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9606858/ https://www.ncbi.nlm.nih.gov/pubmed/36298087 http://dx.doi.org/10.3390/s22207735 |
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