Application of fuzzy neural network in driver fatigue detection

被引：0
|
作者：
Li, Zhi-Chun [1 ,2 ]
He, Ren [1 ]
He, Cui-Qun [2 ]
机构：
[1] School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
[2] Department of Mechanical and Power Engineering, Nanchang Institute of Technology, Nanchang 330029, China
来源：
Jiangsu Daxue Xuebao (Ziran Kexue Ban) / Journal of Jiangsu University (Natural Science Edition) | 2008年 / 29卷 / 02期
关键词：
Algorithms - Applications - Computer simulation - Feature extraction - Fuzzy neural networks - Image processing;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Aimed at the needs of large data, high transmission speed and complex operation, an embedded real-time monitoring system of fatigue driving is developed based on DSP TMS320DM642. In order to reduce the crash accidents caused by fatigue and drowsiness, various fatigue detecting methods are investigated. Fuzzy neural network is used for detecting driver fatigue status, combined with multiple fatigue characteristic cues such as: PERCLOS, AECS, NodFreq and YawnFreq, and the accuracy rate is 88.7%. The results show that the algorithm has preferable effect on fatigue detecting. The developed system has great significance in reducing incident rate of accidents for driver fatigue.
引用
收藏
页码：123 / 126
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