ArticleSensors (Basel, Switzerland)2021
Estimation of the Vehicle Speed Using Cross-Correlation Algorithms and MEMS Wireless Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 46 citations in OpenAlex.
- FZC-TDE: The Algorithm for Real-Time Ultrasonic Stress Measurement at Low Sampling Rates.Micromachines · 2025Article
- Advancements in noise reduction for wheel speed sensing using enhanced LSTM models.Scientific reports · 2025Article
- Vibration Analysis and Vehicle Detection by MEMS Acceleration Sensors Embedded in PCC Pavement.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Optimization of Discrete Element Models for Studying Asphalt Mixture Compaction Characteristics at the Meso-Scale.Sensors (Basel, Switzerland) · 2025Article
- Acoustic Characterization of Transmitted and Received Acoustic Properties of Air-Coupled Ultrasonic Transducers Based on Matching Layer of Organosilicon Hollow Glass Microsphere.Micromachines · 2023Article
- Performance Testing of Micro-Electromechanical Acceleration Sensors for Pavement Vibration Monitoring.Micromachines · 2023Article
- A Time-of-Flight Estimation Method for Acoustic Ranging and Thermometry Based on Digital Lock-In Filtering.Sensors (Basel, Switzerland) · 2022Article
- Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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