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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/1/78 |
| _version_ | 1817995024828203008 |
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| author | Vytautas Markevicius Dangirutis Navikas Mindaugas Zilys Darius Andriukaitis Algimantas Valinevicius Mindaugas Cepenas |
| author_facet | Vytautas Markevicius Dangirutis Navikas Mindaugas Zilys Darius Andriukaitis Algimantas Valinevicius Mindaugas Cepenas |
| author_sort | Vytautas Markevicius |
| collection | DOAJ |
| 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 Kz 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. |
| first_indexed | 2024-04-14T01:59:50Z |
| format | Article |
| id | doaj.art-358118def57f4a6fab7c6a2d6498516a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:59:50Z |
| publishDate | 2016-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-358118def57f4a6fab7c6a2d6498516a2022-12-22T02:18:51ZengMDPI AGSensors1424-82202016-01-011617810.3390/s16010078s16010078Dynamic Vehicle Detection via the Use of Magnetic Field SensorsVytautas Markevicius0Dangirutis Navikas1Mindaugas Zilys2Darius Andriukaitis3Algimantas Valinevicius4Mindaugas Cepenas5Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–418, LT-51368 Kaunas, LithuaniaThe 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 Kz 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.http://www.mdpi.com/1424-8220/16/1/78magnetic fieldAMR sensorsvehicle speed detection |
| spellingShingle | Vytautas Markevicius Dangirutis Navikas Mindaugas Zilys Darius Andriukaitis Algimantas Valinevicius Mindaugas Cepenas Dynamic Vehicle Detection via the Use of Magnetic Field Sensors Sensors magnetic field AMR sensors vehicle speed detection |
| 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 | magnetic field AMR sensors vehicle speed detection |
| url | http://www.mdpi.com/1424-8220/16/1/78 |
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