A Novel Vehicle Classification Using Embedded Strain Gauge Sensors

Abstract

This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle's speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs) were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one) are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves the results of a single sensor data, which is trained on the whole multisensor data set.

Detection and Classification of Bridge Crossing Events With Ground-Based Interferometric Radar Data and Machine Learning Approaches

“…Further sensors applied for event detection and classification are strain gauge sensors. For example, Zhang et al (2008) presents a strain-based method to classify vehicles in five different categories and estimates the speed of each vehicle. The sensors have been embedded below the surface of a state road in China.…”

A Novel Sensor System for Measuring Wheel Loads of Vehicles on Highways

“…Pavement slab width W=4.50 m; L=4.6 m is the length of the pavement; v is the direction of traffic movement; sensors S1, S2, S4, S5 are 25 cm under the edge of pavement; s =4.2 m is the distance between S1 and S4. This layout can be used to measure all the vehicles on the right side of the road [23]. Corresponding vehicle parameters can be extracted from multiple sensors (these will be introduced in Section 3.4).…”

Vehicle Classification Based on FBG Sensor Arrays Using Neural Networks

“…There were also experimental solutions such as the usage of hydro-electric sensors [7] with a bending metal plate at the top of the vessel filled with a specific liquid. Weigh-in-motion technologies measuring specific parameters such as the weight signature could be used, as well as other technologies including fiber optic sensors [8], wireless vibration sensors [9], or using embedded strain gauge sensors [10]. As an additional capability, this could be measured by smart pavements based on conductive cementitious materials [11].…”