计算机与现代化Issue(1)：40-46,67,8.DOI:10.3969/j.issn.1006-2475.2026.01.006
细粒度分心驾驶行为识别方法
Fine-grained Distracted Driving Behavior Recognition
摘要
Abstract
Distracted driving is one of the leading causes of traffic accidents,making the recognition of such behavior critical for improving road safety.However,existing graph convolution-based behavior recognition methods face significant limitations in capturing subtle and complex action features in driving scenarios.To address this,this paper proposes a fine-grained distracted driving behavior recognition network(FG-DDGCN)based on the Hierarchically Decomposed Graph Convolutional Network(HD-GCN).The model incorporates three key modules:Short-Term Explicit Motion Modeling(ST-EM),Multi-Scale Channel-Variable Spatial-Temporal Attention Mechanism(MSCVSTA),and Robust Decouple Loss(RDL),enabling precise recognition of subtle driver actions and significantly improving accuracy.The proposed model is evaluated on the Drive&Act data-set,and ablation experiments are conducted to validate the effectiveness of each module.On the fine-grained activity recognition task,the model achieves the best performance on both the validation and test sets(73.27%and 64.90%,respectively).In the atomic action unit task,the proposed model outperforms existing methods across the action,object,and location dimensions.Ex-perimental results demonstrate that FG-DDGCN exhibits promising capabilities in capturing fine-grained dynamic behavior fea-tures,achieving competitive performance compared to existing methods.This provides a feasible solution for distracted driving behavior recognition and offers valuable insights for future research in related fields.关键词
分心驾驶行为识别/图神经网络/细粒度分类/注意力机制Key words
distracted driving behavior recognition/graph neural networks/fine-grained classification/attention mechanism分类
信息技术与安全科学引用本文复制引用
李桃迎,白文杰,刘文卓..细粒度分心驾驶行为识别方法[J].计算机与现代化,2026,(1):40-46,67,8.基金项目
教育部人文社科基金资助项目(21YJC630066) （21YJC630066）
辽宁省兴辽英才计划(XLYC1907084) （XLYC1907084）
