转弯和直行场景下驾驶员认知分心识别的研究  

A Study of Driver Cognitive Distraction Recognition in Turning and Straight Driving Scenarios

作　　者：曾娟[1,2,3] 许博 王昊[1,2,3] 张洪昌[1,2,3] Zeng Juan;Xu Bo;Wang Hao;Zhang Hongchang(Hubei Key Laboratory of Modern Automotive Components Technology,Wuhan University of Technology,Wuhan 430070;Hubei Collaborative Innovation Center for Automotive Components Technology,Wuhan University of Technology,Wuhan 430070;Hubei Engineering Research Center of New Energy and Intelligent Connected Vehicles,Wuhan University of Technology,Wuhan 430070)

机构地区：[1]武汉理工大学,现代汽车零部件技术湖北省重点实验室,武汉430070 [2]武汉理工大学,汽车零部件技术湖北省协同创新中心,武汉430070 [3]武汉理工大学,湖北省新能源与智能网联车工程技术研究中心,武汉430070

出　　处：《汽车技术》2025年第3期8-14,共7页Automobile Technology

基　　金：教育部创新团队发展计划项目(IRT_17R83);新能源汽车科学与关键技术学科创新引智基地项目(B17034);武汉理工大学重庆研究院科技创新研发项目(YF2021-15)。

摘　　要：为了探寻转弯和直行场景下驾驶员分心驾驶的内在机理,通过驾驶模拟器搭建直行与转弯虚拟场景,采集驾驶员不同驾驶状态的驾驶绩效和眼动信息数据,并使用KNNImputer算法对设备在采集过程中缺失的数据进行插补处理;通过配对样本T检验对时间长度为1 s、重叠率为75%的时间窗口提取的样本数据进行显著性差异分析并提取特征指标;基于该特征指标集合,采用XGBoost分类器构建不同场景下的认知分心识别模型。试验结果表明:相比于直行场景,驾驶员在转弯场景中瞳孔直径变化频率更小、扫视速度更高、注视时间百分比更大,脑力负荷更大;构建的认知分心识别模型在直行场景下的准确率达到91.30%,转弯场景下的准确率为83.28%,转弯场景下认知分心行为危险程度更高,识别更加困难。In order to explore the underlying mechanisms of driver distraction in turning and straight driving scenarios,this study uses a driving simulator to create straight-driving and turning virtual scenarios.It also collects driving performance and eye-movement data of drivers in different driving states.The KNNImputer algorithm is employed to handle missing data during data collection.Then,a paired samples T test is used to analyze significant differences and extract significant difference feature indexes from sample data with a time window of 1 s length and 75%overlap.Based on these features,an XGBoost classifier is used to build cognitive distraction recognition models for different scenarios.The results show that compared with straight driving,drivers in turning scenarios have higher mental workload,indicated by lower pupil diameter change frequency,higher saccade speed and higher fixation duration percentage.The built cognitive distraction recognition model achieves an accuracy of 91.30%for straight-driving and 83.28%for turning scenarios.This suggests that cognitive distraction behavior in turning scenarios is more dangerous and harder to recognize.

关 键 词：转弯场景 直行场景 认知分心 KNNImputer XGBoost 

分 类 号：U461.91[机械工程—车辆工程]