Methodological Study on the Influence of Truck Driving State on the Accuracy of Weigh-in-Motion System

Shuanfeng Zhao,Qing Li,Jianwei Yang,Zhizhong Xing,Zenghui Tang

doi:10.3390/info13030130

Abstract

The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data.

Highlights

To address the above problems, this paper proposes to optimize the network structure based on the YOLOv3 network model to improve the detection accuracy and real-time performance
Driving behaviors vary across drivers for the WIM system in terms of truck weight detection accuracy
This paper proposes a YOLOv3-based truck driving state extraction method, which adds a spatial pyramid pooling network and a cross-stage local network on the basis of the original network to improve the real-time performance of model detection while making the network more lightweight

Summary

Introduction

Road and bridge collapse accidents have occurred in many countries. The collapse of the Porcavella Viaduct in Genoa, Italy, showed that a good structural design is not enough to guarantee the longevity of a bridge. It should be continuously monitored in its operational condition during use to verify that it can carry existing traffic. This will enable the timely detection of damage and defects and the development of maintenance plans to ensure the safety, efficiency and sustainability of the infrastructure [4]. The WIM system can complete the weighing of the whole vehicle during the normal driving process of the vehicle, but in the actual engineering application, the system reveals many problems, such as susceptibility to the driving state of the Published: 3 March 2022

Methods

Results

Conclusion