A Vibration-Based Vehicle Classification System using Distributed Optical Sensing Technology

This paper presents a vibration-based vehicle classification system using distributed optical vibration sensing (DOVS) technology and describes a comprehensive classification method including signal processing and feature extraction. With low maintenance costs, this system can collect vehicle classification data in a larger scale. At first, it utilizes an embedded sensing fiber as a distributed sensor to collect traffic-induced vibration signals, and then extracts several features from the raw signals to estimate axle configurations and identify vehicle categories. At the same time, an empirical mode decomposition (EMD)-based method is applied to reconstruct signals for features extraction, and then several extraction algorithms are proposed to obtain the axle configuration, moving speed, and frequency-domain feature of each vehicle. When all features are extracted, a multi-step classifier is designed to categorize vehicles into different classes. In addition, to evaluate the classification performance of this system, a prototype system was installed on a relief road in Shanghai, China using precast concrete pavement technology. With an overall accuracy of 89%, the test results show a good performance of this classification system.