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Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles
In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection...
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/PMC9572060/ https://www.ncbi.nlm.nih.gov/pubmed/36236417 http://dx.doi.org/10.3390/s22197315 |
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author | Cucor, Boris Petrov, Tibor Kamencay, Patrik Pourhashem, Ghadir Dado, Milan |
author_facet | Cucor, Boris Petrov, Tibor Kamencay, Patrik Pourhashem, Ghadir Dado, Milan |
author_sort | Cucor, Boris |
collection | PubMed |
description | In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision. |
format | Online Article Text |
id | pubmed-9572060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95720602022-10-17 Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles Cucor, Boris Petrov, Tibor Kamencay, Patrik Pourhashem, Ghadir Dado, Milan Sensors (Basel) Article In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings—an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision. MDPI 2022-09-27 /pmc/articles/PMC9572060/ /pubmed/36236417 http://dx.doi.org/10.3390/s22197315 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 | Article Cucor, Boris Petrov, Tibor Kamencay, Patrik Pourhashem, Ghadir Dado, Milan Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title | Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title_full | Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title_fullStr | Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title_full_unstemmed | Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title_short | Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles |
title_sort | physical and digital infrastructure readiness index for connected and automated vehicles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572060/ https://www.ncbi.nlm.nih.gov/pubmed/36236417 http://dx.doi.org/10.3390/s22197315 |
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