Radar sensor based machine learning approach for precise vehicle position estimation
Abstract Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-40961-5 |
| _version_ | 1827634694469976064 |
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| author | Muhammad Sohail Abd Ullah Khan Moid Sandhu Ijaz Ali Shoukat Mohsin Jafri Hyundong Shin |
| author_facet | Muhammad Sohail Abd Ullah Khan Moid Sandhu Ijaz Ali Shoukat Mohsin Jafri Hyundong Shin |
| author_sort | Muhammad Sohail |
| collection | DOAJ |
| description | Abstract Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art. |
| first_indexed | 2024-03-09T15:16:54Z |
| format | Article |
| id | doaj.art-bcb9a45d26f9494fb52cca292133cec1 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:54Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bcb9a45d26f9494fb52cca292133cec12023-11-26T13:02:50ZengNature PortfolioScientific Reports2045-23222023-08-0113111710.1038/s41598-023-40961-5Radar sensor based machine learning approach for precise vehicle position estimationMuhammad Sohail0Abd Ullah Khan1Moid Sandhu2Ijaz Ali Shoukat3Mohsin Jafri4Hyundong Shin5Riphah College of Computing, Riphah International University FaisalabadDepartment of Computer Science, National University of Sciences and Technology Balochistan CampusAustralian e-Health Research Centre, Commonwealth Scientific & Industrial Research Organization (CSIRO)Riphah College of Computing, Riphah International University FaisalabadDepartment of Computer Science, National University of Sciences and Technology Balochistan CampusDepartment of Electronics and Information Convergence Engineering, Kyung Hee UniversityAbstract Estimating vehicles’ position precisely is essential in Vehicular Adhoc Networks (VANETs) for their safe, autonomous, and reliable operation. The conventional approaches used for vehicles’ position estimation, like Global Positioning System (GPS) and Global Navigation Satellite System (GNSS), pose significant data delays and data transmission errors, which render them ineffective in achieving precision in vehicles’ position estimation, especially under dynamic environments. Moreover, the existing radar-based approaches proposed for position estimation utilize the static values of range and azimuth, which make them inefficient in highly dynamic environments. In this paper, we propose a radar-based relative vehicle positioning estimation method. In the proposed method, the dynamic range and azimuth of a Frequency Modulated Continuous Wave radar is utilized to precisely estimate a vehicle’s position. In the position estimation process, the speed of the vehicle equipped with the radar sensor, called the reference vehicle, is considered such that a change in the vehicle’s speed changes the range and azimuth of the radar sensor. For relative position estimation, the distance and relative speed between the reference vehicle and a nearby vehicle are used. To this end, only those vehicles are considered that have a higher possibility of coming in contact with the reference vehicle. The data recorded by the radar sensor is subsequently utilized to calculate the precision and intersection Over Union (IOU) values. You Only Look Once (YOLO) version 4 is utilized to calculate precision and IOU values from the data captured using the radar sensor. The performance is evaluated under various real-time traffic scenarios in a MATLAB-based simulator. Results show that our proposed method achieves 80.0% precision in position estimation and obtains an IOU value up to 87.14%, thereby outperforming the state-of-the-art.https://doi.org/10.1038/s41598-023-40961-5 |
| spellingShingle | Muhammad Sohail Abd Ullah Khan Moid Sandhu Ijaz Ali Shoukat Mohsin Jafri Hyundong Shin Radar sensor based machine learning approach for precise vehicle position estimation Scientific Reports |
| title | Radar sensor based machine learning approach for precise vehicle position estimation |
| title_full | Radar sensor based machine learning approach for precise vehicle position estimation |
| title_fullStr | Radar sensor based machine learning approach for precise vehicle position estimation |
| title_full_unstemmed | Radar sensor based machine learning approach for precise vehicle position estimation |
| title_short | Radar sensor based machine learning approach for precise vehicle position estimation |
| title_sort | radar sensor based machine learning approach for precise vehicle position estimation |
| url | https://doi.org/10.1038/s41598-023-40961-5 |
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