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

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.

The Design and Evaluation of a Telematic Automated System of Weight Control for Heavy Vehicles

“…Many studies have analyzed existing problems related to congestion and damage to road infrastructure in various regions, which emphasizes the relevance of this research [14][15][16].…”

Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal

“…Subsequently, the introduction of technologies like wavelet transform made signal analysis more flexible and efficient, enabling accurate capture of dynamic characteristics and periodic changes in the signal [2]. In recent years, the application of artificial intelligence technologies such as machine learning and deep learning has brought new opportunities for dynamic weighing, allowing for the automatic extraction of signal features and patterns by learning from large amounts of data, achieving high-precision weight measurement, and noise suppression [3][4][5].…”