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

Cheng Zhang1, Shihui Shen2, Hai Huang2

  • 1Department of Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USA.

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|April 3, 2021
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Summary

This study introduces a novel wireless sensor (Smartrock) for estimating vehicle speed using cross-correlation algorithms. The Smoothed Coherence Transform (SCOT) algorithm demonstrated the best accuracy and stability for traffic speed estimation.

Keywords:
accelerated pavement testingcross-correlationmicro-electromechanical sensorspeed estimationwireless sensor

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Area of Science:

  • Civil Engineering
  • Transportation Engineering
  • Sensor Technology

Background:

  • Accurate traffic data is vital for pavement design, management, and health monitoring.
  • Existing in-pavement sensors primarily focus on volume and load amplitude, with less attention on vehicle speed estimation algorithms.
  • Vehicle speed is a crucial parameter for understanding traffic dynamics and pavement response.

Purpose of the Study:

  • To develop and evaluate algorithms for estimating vehicle speed using novel wireless micro-electromechanical sensors (MEMS).
  • To compare the performance of different cross-correlation algorithms (NCC, SCOT, PHAT) for traffic speed estimation.
  • To assess the impact of noise on the accuracy and stability of speed estimation algorithms.

Main Methods:

  • Utilized a novel wireless MEMS sensor, Smartrock, to collect triaxial acceleration, rotation, and stress data.
  • Applied three cross-correlation algorithms: Normalized Cross-Correlation (NCC), Smoothed Coherence Transform (SCOT), and Phase Transform (PHAT).
  • Evaluated algorithm performance using Signal-Noise-Ratio (SNR) and Mean Relative Error (MRE) metrics.

Main Results:

  • Both correlated and independent noise significantly impacted field data accuracy.
  • The SCOT algorithm yielded the highest SNR and lowest MRE, indicating superior stability and accuracy.
  • The study successfully estimated vehicle speeds up to 50 km/h.

Conclusions:

  • The SCOT algorithm is recommended for accurate and stable vehicle speed estimation in pavement monitoring.
  • Wireless MEMS sensors like Smartrock are effective for capturing data necessary for speed estimation.
  • Further research is required to validate the performance of these algorithms for speeds exceeding 50 km/h.