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Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors

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dc.contributor.authorZhang, Chengen
dc.contributor.authorShen, Shihuien
dc.contributor.authorHuang, Haien
dc.contributor.authorWang, Linbingen
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2021-03-15T11:41:16Zen
dc.date.available2021-03-15T11:41:16Zen
dc.date.issued2021-03-02en
dc.date.updated2021-03-12T14:39:19Zen
dc.description.abstractTraffic 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.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationZhang, C.; Shen, S.; Huang, H.; Wang, L. Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors. Sensors 2021, 21, 1721.en
dc.identifier.doihttps://doi.org/10.3390/s21051721en
dc.identifier.urihttp://hdl.handle.net/10919/102710en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectspeed estimationen
dc.subjectmicro-electromechanical sensoren
dc.subjectwireless sensoren
dc.subjectcross-correlationen
dc.subjectaccelerated pavement testingen
dc.titleEstimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensorsen
dc.title.serialSensorsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)
Scholarly Works, Civil and Environmental Engineering