计算机工程与科学2025，Vol.47Issue(3)：548-560,13.DOI:10.3969/j.issn.1007-130X.2025.03.016
基于驾驶上下文感知的驾驶员识别模型
Driver identification model based on driving context-aware
摘要
Abstract
With the increasing awareness of privacy protection,identifying car drivers using vehicle trajectory data has become a hot topic in vehicle data analysis.However,existing models struggle to accurately capture the relationship between driving style and driving context,resulting in low identifica-tion accuracy.Therefore,a driving context-aware driver identification model(CDIM)is proposed.CDIM utilizes trajectory data to calculate vehicle motion features and obtains travel routes through road network matching.It also designs a road segment information embedding module based on a bidirection-al Transformer,which generates embeddings for each road segment in the travel route by fusing features of adjacent road segments.Then,a convolutional cross-modal attention fusion module is used to com-bine road segment features with motion features,achieving efficient fusion of the two.Additionally,ex-ternal factor features are incorporated to comprehensively capture the influence of driving context on driving style.Experimental results on public datasets show that CDIM achieves a identification accuracy of 68.54％,which is an improvement of 8.14％and 4.81％compared to RM-Driver and Doufu,respec-tively,demonstrating higher driver identification accuracy.关键词
驾驶员识别/表示学习/上下文感知/特征融合Key words
driver identification/representation learning/context-aware/feature fusion分类
信息技术与安全科学引用本文复制引用
杨林,张磊,刘佰龙,梁志贞,张雪飞..基于驾驶上下文感知的驾驶员识别模型[J].计算机工程与科学,2025,47(3):548-560,13.基金项目
中国矿业大学建设双一级专项资金(2018ZZCX14) （2018ZZCX14）
