Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array
The increasing presence of vehicles on roads necessitates intelligent traffic management solutions in areas where video cameras cannot be utilized. Currently, there are limited choices for depersonalized vehicle reidentification systems. This paper introduces a system that later will be used for veh...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10130572/ |
| _version_ | 1797812847529426944 |
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| author | Juozas Balamutas Dangirutis Navikas Vytautas Markevicius Mindaugas Cepenas Algimantas Valinevicius Mindaugas Zilys Michal Frivaldsky Zhixiong Li Darius Andriukaitis |
| author_facet | Juozas Balamutas Dangirutis Navikas Vytautas Markevicius Mindaugas Cepenas Algimantas Valinevicius Mindaugas Zilys Michal Frivaldsky Zhixiong Li Darius Andriukaitis |
| author_sort | Juozas Balamutas |
| collection | DOAJ |
| description | The increasing presence of vehicles on roads necessitates intelligent traffic management solutions in areas where video cameras cannot be utilized. Currently, there are limited choices for depersonalized vehicle reidentification systems. This paper introduces a system that later will be used for vehicle reidentification. The system uses anisotropic magnetoresistive sensors and is based on the hypothesis that each vehicle leaves unique magnetic signatures which can be used for comparison and matching. Vehicle location on the road perpendicular to sensor array detection methodology is presented in this work. An array of magnetic sensors is installed in asphalt across the vehicle’s driving direction. The system continuously measures Earth’s natural magnetic field and detects distortions when vehicles pass an array of a sensors and then logs magnetic signatures. Useful parameters from raw sensor axes are calculated– modules and derivatives. Applying signal-to-noise ratio calculation for module derivatives between ambient noise and signal gives important features for neural network input. Different types of neural network architectures and output result interpretation techniques are investigated. Further, after evaluating network output it is possible to label sensor nodes that are directly beneath the vehicle. Experiment results show that implemented algorithm is highly sufficient for valid sensors under the vehicle selection. Correct sensor selection is important for further re-identification algorithms. |
| first_indexed | 2024-03-13T07:44:29Z |
| format | Article |
| id | doaj.art-0e8b16aa75a54358ba1afb962587a6c4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T07:44:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0e8b16aa75a54358ba1afb962587a6c42023-06-02T23:00:14ZengIEEEIEEE Access2169-35362023-01-0111509845099310.1109/ACCESS.2023.327898610130572Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor ArrayJuozas Balamutas0https://orcid.org/0000-0002-5569-6637Dangirutis Navikas1Vytautas Markevicius2https://orcid.org/0000-0001-8856-1037Mindaugas Cepenas3Algimantas Valinevicius4Mindaugas Zilys5Michal Frivaldsky6https://orcid.org/0000-0001-6138-3103Zhixiong Li7https://orcid.org/0000-0003-4067-0669Darius Andriukaitis8https://orcid.org/0000-0002-9862-8917Department of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaDepartment of Electronics and Mechatronics, Faculty of Electrical Engineering and Information Technologies, University of Žilina, Žilina, SlovakiaDepartment of Manufacturing Engineering and Automation Products, Opole University of Technology, Opole, PolandDepartment of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, LithuaniaThe increasing presence of vehicles on roads necessitates intelligent traffic management solutions in areas where video cameras cannot be utilized. Currently, there are limited choices for depersonalized vehicle reidentification systems. This paper introduces a system that later will be used for vehicle reidentification. The system uses anisotropic magnetoresistive sensors and is based on the hypothesis that each vehicle leaves unique magnetic signatures which can be used for comparison and matching. Vehicle location on the road perpendicular to sensor array detection methodology is presented in this work. An array of magnetic sensors is installed in asphalt across the vehicle’s driving direction. The system continuously measures Earth’s natural magnetic field and detects distortions when vehicles pass an array of a sensors and then logs magnetic signatures. Useful parameters from raw sensor axes are calculated– modules and derivatives. Applying signal-to-noise ratio calculation for module derivatives between ambient noise and signal gives important features for neural network input. Different types of neural network architectures and output result interpretation techniques are investigated. Further, after evaluating network output it is possible to label sensor nodes that are directly beneath the vehicle. Experiment results show that implemented algorithm is highly sufficient for valid sensors under the vehicle selection. Correct sensor selection is important for further re-identification algorithms.https://ieeexplore.ieee.org/document/10130572/Magnetic field measurementmagnetic signaturevehicle re-identificationintelligent transportation systems |
| spellingShingle | Juozas Balamutas Dangirutis Navikas Vytautas Markevicius Mindaugas Cepenas Algimantas Valinevicius Mindaugas Zilys Michal Frivaldsky Zhixiong Li Darius Andriukaitis Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array IEEE Access Magnetic field measurement magnetic signature vehicle re-identification intelligent transportation systems |
| title | Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array |
| title_full | Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array |
| title_fullStr | Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array |
| title_full_unstemmed | Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array |
| title_short | Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array |
| title_sort | passing vehicle road occupancy detection using the magnetic sensor array |
| topic | Magnetic field measurement magnetic signature vehicle re-identification intelligent transportation systems |
| url | https://ieeexplore.ieee.org/document/10130572/ |
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