Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors
Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This rese...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/5/1721 |
| _version_ | 1797416006700761088 |
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| author | Cheng Zhang Shihui Shen Hai Huang Linbing Wang |
| author_facet | Cheng Zhang Shihui Shen Hai Huang Linbing Wang |
| author_sort | Cheng Zhang |
| collection | DOAJ |
| description | Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation. |
| first_indexed | 2024-03-09T05:57:40Z |
| format | Article |
| id | doaj.art-d9ad20c1b7934dc48c62f221a778ef02 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:57:40Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d9ad20c1b7934dc48c62f221a778ef022023-12-03T12:12:30ZengMDPI AGSensors1424-82202021-03-01215172110.3390/s21051721Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless SensorsCheng Zhang0Shihui Shen1Hai Huang2Linbing Wang3Department of Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USARail Transportation Engineering, Penn State Altoona, Altoona, PA 16601, USARail Transportation Engineering, Penn State Altoona, Altoona, PA 16601, USADepartment of Civil and Environmental Engineering, Virginia Tech University, Blacksburg, VA 24060, USATraffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.https://www.mdpi.com/1424-8220/21/5/1721speed estimationmicro-electromechanical sensorwireless sensorcross-correlationaccelerated pavement testing |
| spellingShingle | Cheng Zhang Shihui Shen Hai Huang Linbing Wang Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors Sensors speed estimation micro-electromechanical sensor wireless sensor cross-correlation accelerated pavement testing |
| title | Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors |
| title_full | Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors |
| title_fullStr | Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors |
| title_full_unstemmed | Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors |
| title_short | Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors |
| title_sort | estimation of the vehicle speed using cross correlation algorithms and mems wireless sensors |
| topic | speed estimation micro-electromechanical sensor wireless sensor cross-correlation accelerated pavement testing |
| url | https://www.mdpi.com/1424-8220/21/5/1721 |
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