Tire–Pavement Contact-Aware Weight Estimation for Multi-Sensor WIM Systems

Abstract

Accurately estimating the weight of a moving vehicle at normal speed remains a challenging problem due to the complex vehicle dynamics and vehicle–pavement interaction. The weighing technique based on multiple sensors has proven to be an effective approach to this task. To improve the accuracy of weigh-in-motion (WIM) systems, this paper proposes a neural network-based method integrating identification and predication. A backpropagation neural network for signal classification (BPNN-i) was designed to identify ideal samples acquired by load sensors closest to the tire-pavement contact area. After that, ideal samples were used to predict the gross vehicle weight by using another backpropagation neural network (BPNN-e). The dataset for training and evaluation was collected from a multiple-sensor WIM (MS-WIM) system deployed in a public road. In our experiments, 96.89% of samples in the test set had an estimation error of less than 5%.