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Dynamic Vehicle Detection via the Use of Magnetic Field Sensors
The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In acco...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732111/ https://www.ncbi.nlm.nih.gov/pubmed/26797615 http://dx.doi.org/10.3390/s16010078 |
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author | Markevicius, Vytautas Navikas, Dangirutis Zilys, Mindaugas Andriukaitis, Darius Valinevicius, Algimantas Cepenas, Mindaugas |
author_facet | Markevicius, Vytautas Navikas, Dangirutis Zilys, Mindaugas Andriukaitis, Darius Valinevicius, Algimantas Cepenas, Mindaugas |
author_sort | Markevicius, Vytautas |
collection | PubMed |
description | The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and K(z) criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m. |
format | Online Article Text |
id | pubmed-4732111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-47321112016-02-12 Dynamic Vehicle Detection via the Use of Magnetic Field Sensors Markevicius, Vytautas Navikas, Dangirutis Zilys, Mindaugas Andriukaitis, Darius Valinevicius, Algimantas Cepenas, Mindaugas Sensors (Basel) Article The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and K(z) criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m. MDPI 2016-01-19 /pmc/articles/PMC4732111/ /pubmed/26797615 http://dx.doi.org/10.3390/s16010078 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Markevicius, Vytautas Navikas, Dangirutis Zilys, Mindaugas Andriukaitis, Darius Valinevicius, Algimantas Cepenas, Mindaugas Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title | Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title_full | Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title_fullStr | Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title_full_unstemmed | Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title_short | Dynamic Vehicle Detection via the Use of Magnetic Field Sensors |
title_sort | dynamic vehicle detection via the use of magnetic field sensors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732111/ https://www.ncbi.nlm.nih.gov/pubmed/26797615 http://dx.doi.org/10.3390/s16010078 |
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