Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing

This study introduces the Smart Road Stud (SRS), an innovative Anisotropic Magneto-Resistive (AMR) detector strategically placed on lane markings for advanced traffic monitoring. Unlike traditional detectors, the SRS, when deployed in pairs or more, facilitates simultaneous vehicle detection. Analysis reveals that single AMR detectors often face challenges, such as interference signals and misidentification of double-vehicle signals. To address these issues, a method leveraging multiple AMR detectors is proposed. Experimental results demonstrate over 95% accuracy in identifying interference signals and distinguishing double-vehicle signals. The approach achieves a remarkable 98.7% accuracy in lane-level traffic volume estimation. The SRS outperforms conventional detectors, showcasing its potential to enhance accuracy and versatility in traffic monitoring.

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Publication Details

Published
2024-10-07
DOI
https://doi.org/10.1109/vtc2024-fall63153.2024.10757979
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
Field-Weighted Citation Impact
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article

Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing

John W. M. Cheng, Wei Quan, Hao Li, Hua Wang et al.
Energy Efficient Wireless Sensor Networks
article

Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing

John W. M. Cheng, Wei Quan, Hao Li, Hua Wang, Yanli Sun, Jiayu Sun, Yimeng Feng
article en

Abstract

This study introduces the Smart Road Stud (SRS), an innovative Anisotropic Magneto-Resistive (AMR) detector strategically placed on lane markings for advanced traffic monitoring. Unlike traditional detectors, the SRS, when deployed in pairs or more, facilitates simultaneous vehicle detection. Analysis reveals that single AMR detectors often face challenges, such as interference signals and misidentification of double-vehicle signals. To address these issues, a method leveraging multiple AMR detectors is proposed. Experimental results demonstrate over 95% accuracy in identifying interference signals and distinguishing double-vehicle signals. The approach achieves a remarkable 98.7% accuracy in lane-level traffic volume estimation. The SRS outperforms conventional detectors, showcasing its potential to enhance accuracy and versatility in traffic monitoring.

Harbin Institute of Technology (CN), Macquarie University (AU)
Affordable and clean energy
Openalex Percentile: Top 70%
Energy Efficient Wireless Sensor Networks
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Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing — John W. M. Cheng, Wei Quan, et al. · (2024) | TGRS Research Map | TGRS