Identification of vehicle axle loads based on visual measurement

Abstract

Monitoring vehicle axle loads is very important for preventing infrastructure degradation and traffic accidents. However, developing a cost-effective, flexible, and accurate axle load identification technology still remains challenging. In this study, a vehicle axle load identification method based on visual measurement is proposed. The principle of the method is that each axle load of the vehicle is determined based on the ratio of vehicular sprung mass to unsprung mass and the centroid position of the sprung mass, which can be predicted from the visually captured free decaying vibrations of the vehicle as it passes over a speed bump. To be more specific, the vibration responses of a vehicle passing over a speed bump is firstly measured by a camera, and the vehicle system matrix, which is composed of the mass matrix, stiffness matrix and damping matrix, can be obtained from the vehicle responses. The axle load can then be determined based on the element ratios in the system matrix of the vehicle whose unsprung masses are known. The performance of the proposed method is evaluated through numerical simulations and field tests. Results show that the identified axle loads agree well with the corresponding true values, proving the effectiveness and accuracy of the proposed method. This study has demonstrated a novel application of the computer vision technology to identify the axle loads of moving vehicles. The proposed method does not require the installation of sensors on the roadway or on the vehicle, making it a promising alternative for traditional weigh-in-motion systems.