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In order to address the issue of human-machine collaboration failure in the takeover decision-making of autonomous driving and achieve precise recognition of the driver's cognitive state, this paper simulates three typical scenarios, night high-speed curves, mobile phone distracted driving, and combined scenarios of strong light and heavy rain, collecting and analyzing the dynamic interaction data of driving behavior and eye movement features construct a dynamic weight allocation(DWA) distribution feature fusion framework for multimodal perception and cognition collaboration, constructed DWA-RF model, and explore the dynamic mechanism of the driver's cognitive state and takeover decision-making behavior in complex environments. The results show that the distracted state significantly prolongs the takeover time. In scenes of strong light and heavy rain, the superimposition of distraction and environmental pressure leads to a sharp reduction in the range of scanning. Fatigue causes an increase in lane departure distance, accompanied by a reduction in pupil diameter and abnormal scanning behavior. The cognitive state classification accuracy of DWA-RF constructed in this paper has reached 93.6%, verifying the effectiveness of this model in identifying the driver's cognitive state for autonomous driving takeover decisions.

, authors=Shuyu SHAO^{1, 2}, Yanping LI¹, Jiaqi HAN¹, authorsList=Shuyu SHAO, Yanping LI, Jiaqi HAN, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608163661164940, articleId=1281608162436428168, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=自动驾驶接管决策中驾驶员认知状态识别研究, columnId=1281608091288453145, journalTitle=中国安全科学学报, columnName=智能安全技术, runingTitle=null, highlight=null, articleAbstract=

为解决自动驾驶接管决策中人机协作失效问题，并精准识别驾驶员认知状态，模拟夜间高速弯道、手机分心驾驶及强光暴雨复合等3类典型场景，采集并分析驾驶行为与眼动特征的动态交互数据，构建多模态感知与认知协同的动态权重分配（DWA）特征融合框架及DWA随机森林（RF）模型，探究复杂环境下驾驶员认知状态与接管决策行为的动态作用机制。结果表明：分心状态显著延长接管时间；在强光暴雨场景中，分心与环境压力叠加导致扫视幅度锐减；疲劳状态则引发车道偏离距离增加，并伴随瞳孔直径缩小及异常扫视行为。DWA-RF模型的认知状态分类准确率达到了93.6%，验证了该模型在自动驾驶接管决策中驾驶员认知状态识别的有效性。

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²Beijing Key Laboratory of Intelligent Logistics System, Beijing 101149, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608164239978904, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608162436428168, authorId=1281608164080595348, language=CN, stringName=邵舒羽副教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹北京物资学院 智能工程与供应链创新学院，北京 101149
²智能物流系统北京市重点实验室，北京 101149, bio={"content":"

邵舒羽（1989—），男，河南周口人，博士，副教授，主要从事应急物流、人因工程、应急管理等方面的研究。E-mail: shaoshuyu@bwu.edu.cn。

"}, bioImg=null, bioContent=

邵舒羽（1989—），男，河南周口人，博士，副教授，主要从事应急物流、人因工程、应急管理等方面的研究。E-mail: shaoshuyu@bwu.edu.cn。

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