Weight estimation model for trucks integrating multi-head attention mechanism

In order to improve the real-time truck load estimation of convenience and accuracy, and to help to develop real-time monitoring for truck loads in a large-scale low-grade highway networks, this paper proposed a weight estimation model(Mix-MAN)for trucks integrating the multi-head attention mechanism based on the interaction effect of dynamic and static information of trucks. Firstly, multi-head attention was introduced into the model to enhance the network's ability to extract kinematic time series features;secondly, a stacked auto-encoder was used to capture the static features of trucks; finally, a feature fusion structure was designed to extract dynamic features and static features, establish the nonlinear mapping relationship between input features and weight estimation, and then to obtain the final weight estimation result of trucks. The experimental results show that compared with the MAN model without considering static information of truck, the mean absolute value error of Mix-MAN is reduced by 6%, root mean square error is reduced by 5%, mean absolute percentage error is reduced by 0.5%. The model in this paper can provide technical support for the cargo transport supervision of highway and road maintenance. © 2024 Editorial Board of Jilin University. All rights reserved.