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

作　　者：杨林[1,2] 张磊 刘佰龙[1,2] 梁志贞[1,2] 张雪飞[3] YANG Lin;ZHANG Lei;LIU Bailong;LIANG Zhizhen;ZHANG Xuefei(Engineering Research Center for Mine Digitalization of Ministry of Education,Chian University of Mining and Technology,Xuzhou 221116;School of Computer Science&Technology,China University of Mining and Technology,Xuzhou 221116;Jiangsu Hengwang Digital Technology Co.,Ltd.Suzhou 215000,China)

机构地区：[1]中国矿业大学矿山数字化教育部工程研究中心,江苏徐州221116 [2]中国矿业大学计算机科学与技术学院,江苏徐州221116 [3]江苏恒旺数字科技有限责任公司,江苏苏州215000

出　　处：《计算机工程与科学》2025年第3期548-560,共13页Computer Engineering & Science

基　　金：中国矿业大学建设双一级专项资金(2018ZZCX14)。

摘　　要：随着隐私保护意识的提升,利用车辆轨迹识别汽车驾驶员已成为车辆数据分析热点。然而,现有模型难以准确捕捉驾驶风格与驾驶上下文之间的关系,导致识别准确率不高。因此,提出基于驾驶上下文感知的驾驶员识别模型CDIM。CDIM利用轨迹数据计算车辆运动特征,同时通过路网匹配获取出行路线,并设计基于双向Transformer的路段信息嵌入模块,为出行路线中每一段路段生成融合邻接路段特征的嵌入。然后,通过卷积跨模态注意力融合模块结合路段特征与运动特征,实现二者的高效融合。此外,结合外部因素特征,全面捕捉驾驶上下文对驾驶风格的影响。在公开数据集上的实验结果表明,CDIM的识别准确率为68.54%,相较于RM-Driver与Doufu分别提高了8.14%和4.81%,具有更高的驾驶员识别准确率。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 identification 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 bidirectional 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 combine road segment features with motion features,achieving efficient fusion of the two.Additionally,external 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,respectively,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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]