Driver Drowsiness Detection Based on Multisource Information

被引：40

作者：

Cheng, Bo ^{[2
]}

Zhang, Wei ^{[2
]}

Lin, Yingzi ^{[1
]}

Feng, Ruijia ^{[2
]}

Zhang, Xibo ^{[2
]}

机构：

[1] Northeastern Univ, Dept Mech & Ind Engn, Boston, MA 02115 USA

[2] Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China

来源：

HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES | 2012年 / 22卷 / 05期

基金：

国家高技术研究发展计划(863计划); 美国国家科学基金会;

关键词：

ITS; Automotive active safety; Driver assistance system; Drowsy driving; Multisource information fusion; FATIGUE; TIME; FUSION;

D O I：

10.1002/hfm.20395

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Driver drowsiness is one of the major causes of on-road accidents. Abnormal eye behavior, steering wheel activity, and vehicle trajectory during different drowsiness stages were studied in detail to overcome the limitations of single-sensor approaches. Some measures, such as percentage of eyelid closure, maximum close duration, and percentage of nonsteering were analyzed using analysis of variance (ANOVA) methods. Moreover, a two-stage data fusion framework was developed for the modeling combination of information from different sources. Fisher's linear discriminant was implied as the feature-level fusion method, and Dempster-Shafer evidence theory was introduced in the decision-level fusion process. The results suggest that the recognition system proposed here provided 90.7% accuracy. The reliability and accuracy of the fusion method were significantly higher than those of single sensors. (C) 2012 Wiley Periodicals, Inc.

引用

页码：450 / 467

页数：18

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