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An innovative traffic light recognition method using vehicular ad-hoc networks
Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006197/ https://www.ncbi.nlm.nih.gov/pubmed/36899122 http://dx.doi.org/10.1038/s41598-023-31107-8 |
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author | Al-Ezaly, Esraa M. El-Bakry, Hazem Abo-Elfetoh, Ahmed Elhishi, Sara |
author_facet | Al-Ezaly, Esraa M. El-Bakry, Hazem Abo-Elfetoh, Ahmed Elhishi, Sara |
author_sort | Al-Ezaly, Esraa |
collection | PubMed |
description | Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second. |
format | Online Article Text |
id | pubmed-10006197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100061972023-03-12 An innovative traffic light recognition method using vehicular ad-hoc networks Al-Ezaly, Esraa M. El-Bakry, Hazem Abo-Elfetoh, Ahmed Elhishi, Sara Sci Rep Article Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second. Nature Publishing Group UK 2023-03-10 /pmc/articles/PMC10006197/ /pubmed/36899122 http://dx.doi.org/10.1038/s41598-023-31107-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Al-Ezaly, Esraa M. El-Bakry, Hazem Abo-Elfetoh, Ahmed Elhishi, Sara An innovative traffic light recognition method using vehicular ad-hoc networks |
title | An innovative traffic light recognition method using vehicular ad-hoc networks |
title_full | An innovative traffic light recognition method using vehicular ad-hoc networks |
title_fullStr | An innovative traffic light recognition method using vehicular ad-hoc networks |
title_full_unstemmed | An innovative traffic light recognition method using vehicular ad-hoc networks |
title_short | An innovative traffic light recognition method using vehicular ad-hoc networks |
title_sort | innovative traffic light recognition method using vehicular ad-hoc networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006197/ https://www.ncbi.nlm.nih.gov/pubmed/36899122 http://dx.doi.org/10.1038/s41598-023-31107-8 |
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