Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues | Zendy

Mingheng Zhang | Zendy; Gang Longhui | Zendy; Zhe Wang | Zendy; Xiaoming Xu | Zendy; Baozhen Yao | Zendy; Liping Zhou | Zendy

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Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues

Author(s) -

Mingheng Zhang,

Gang Longhui,

Zhe Wang,

Xiaoming Xu,

Baozhen Yao,

Liping Zhou

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/385716

Subject(s) - support vector machine , computer science , curse of dimensionality , artificial intelligence , state (computer science) , machine learning , engineering , algorithm

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

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