Vehicle weight estimation plays a crucial role in evaluating the fatigue life of bridges and pavements. Traditional Weigh-in-Motion (WIM) systems, which require sensors on bridges or pavements, are costly and have limited deployment flexibility. Recently, computer vision-based approaches have been explored to estimate vehicle weight from vibration characteristics of vehicles without the need for such sensors. However, most existing methods employ simplified vehicle models that ignore damping effects, resulting in limited estimation accuracy due to the inherently high damping ratio of vehicles. Moreover, these methods often rely on prior knowledge of the vehicle’s center of gravity (CoG), further restricting their general applicability. This study proposes an enhanced vision-based framework that overcomes these limitations. Vehicle vibrations are captured through object detection, filtering, and subpixel displacement estimation. The System Realization through Information Matrix (SRIM) method is then used to identify damped bouncing and pitching modes with complex mode shapes. Based on the state-space equations, a complex matrix formulation incorporating these damped modes enables simultaneous estimation of vehicle and axle weights, achieving over 84% accuracy in field tests.
Ishii et al. (Sun,) studied this question.