Driving Behavior Analysis Based on LRCN Model and Attention Mechanism
ZHANG Yaofeng
HU Ruize
Abstract：To address the problems of low recognition efficiency and poor recognition accuracy of current computer vi-sion-based irregular driving behavior analysis models,this paper proposes an improved LRCN deep learning model,which can pro-cess both temporal video and single-frame picture,by replacing the original LSTM module with the GRU module.By using the State-Farm dataset from Kaggle as the training data,the improved LRCN model with fused attention mechanism is applied to form a recog-nition method for irregular driving behaviors.The experimental results demonstrate that the improved framework enjoys advantages compared to the original LRCN model in terms of both accuracy and training speed.
Keywords：driving behaviordeep learningconvolutional neural networkgated recurrent unitattention mechanism
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3202-3207 )