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Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues
Joint Authors
Wang, Zhe
Baozhen, Yao
Zhang, Mingheng
Longhui, Gang
Xu, Xiaoming
Zhou, Liping
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-13
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper presents a hybrid model for early onset prediction of driver fatigue, which is the major reason of severe traffic accidents.
The proposed method divides the prediction problem into three stages, that is, SVM-based model for predicting the early onset driver fatigue state, GA-based model for optimizing the parameters in the SVM, and PCA-based model for reducing the dimensionality of the complex features datasets.
The model and algorithm are illustrated with driving experiment data and comparison results also show that the hybrid method can generally provide a better performance for driver fatigue state prediction.
American Psychological Association (APA)
Zhang, Mingheng& Longhui, Gang& Wang, Zhe& Xu, Xiaoming& Baozhen, Yao& Zhou, Liping. 2014. Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-467953
Modern Language Association (MLA)
Zhang, Mingheng…[et al.]. Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-467953
American Medical Association (AMA)
Zhang, Mingheng& Longhui, Gang& Wang, Zhe& Xu, Xiaoming& Baozhen, Yao& Zhou, Liping. Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-467953
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-467953