TY - JOUR A2 - Mu, Rui AU - Zhang, Mingheng AU - Longhui, Gang AU - Wang, Zhe AU - Xu, Xiaoming AU - Yao, Baozhen AU - Zhou, Liping PY - 2014 DA - 2014/07/13 TI - Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues SP - 385716 VL - 2014 AB - 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. SN - 1024-123X UR - https://doi.org/10.1155/2014/385716 DO - 10.1155/2014/385716 JF - Mathematical Problems in Engineering PB - Hindawi Publishing Corporation KW - ER -