疲劳驾驶检测研究综述  

Review on Fatigue Driving Detection

作　　者：成亚玲[1] 谭爱平[1] CHENG Yaling;TAN Aiping(Hunan Industry Polytechnic,Changsha,Hunan Province,410208 China)

机构地区：[1]湖南工业职业技术学院,湖南长沙410208

出　　处：《科技创新导报》2021年第24期182-187,共6页Science and Technology Innovation Herald

基　　金：湖南省教育厅科学研究项目(项目编号:17B079)。

摘　　要：疲劳驾驶是导致交通事故的重要原因,严重影响公众的交通安全出行,受到全球各国的高度重视,科研人员进行了大量研究。相关文献从生理特征信息、车辆行为特征信息、视觉特征信息和多特征信息融合等4个维度进行了梳理,并指出了不同检测方法的优缺点。最后,基于不同场景的应用需求,指出了基于深度学习、神经网络与多特征信息融合的疲劳检测研究将是未来研究的重点和热点。Fatigue driving is an important cause of traffic accidents,which seriously affects the public's traffic safety.Countries all over the world attach great importance to it,and researchers have carried out a lot of research about it.The relevant literature combs the four dimensions of physiological feature information,vehicle behavior feature information,visual feature information and multi feature information fusion,and the advantages and disadvantages of different detection methods are pointed out.Finally,based on the application requirements of different scenarios,it is pointed out that the research on fatigue detection based on deep learning,neural network and multi feature information fusion will be the focus and hotspot of future research.

关 键 词：驾驶行为 疲劳检测 检测方法 信息融合 预测 

分 类 号：U491.254[交通运输工程—交通运输规划与管理] TP391[交通运输工程—道路与铁道工程]