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
相关论文
共 50 条
  • [21] Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing
    Sheykhivand, Sobhan
    Rezaii, Tohid Yousefi
    Meshgini, Saeed
    Makoui, Somaye
    Farzamnia, Ali
    SUSTAINABILITY, 2022, 14 (05)
  • [22] A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network
    Cheng, Wangfeng
    Wang, Xuanyao
    Mao, Bangguo
    VISUAL COMPUTER, 2024, 40 (04): : 2419 - 2441
  • [23] A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network
    Wangfeng Cheng
    Xuanyao Wang
    Bangguo Mao
    The Visual Computer, 2024, 40 : 2419 - 2441
  • [24] Driver Distraction Detection Using Deep Neural Network
    Kouchak, Shokoufeh Monjezi
    Gaffar, Ashraf
    MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, 2019, 11943 : 13 - 23
  • [25] Driver Sleepiness Detection Using LSTM Neural Network
    Deng, Yini
    Jiao, Yingying
    Lu, Bao-Liang
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT IV, 2018, 11304 : 622 - 633
  • [26] Detection of Distracted Driver using Convolutional Neural Network
    Baheti, Bhakti
    Gajre, Suhas
    Talbar, Sanjay
    PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2018, : 1145 - 1151
  • [27] Driver Fatigue Expression Recognition Research Based on Convolutional Neural Network
    Huang, Mengtao
    Wang, Yi
    2018 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2018), 2018, : 6 - 9
  • [28] A neural system for eye detection in a driver vigilance application
    D'Orazio, T
    Leo, M
    Spagnolo, P
    Guaragnella, C
    ITSC 2004: 7TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, PROCEEDINGS, 2004, : 320 - 325
  • [29] Application of neural network to the evolution of fatigue cracks
    Chen, Xin
    Huang, Hongzhong
    Sun, Daoheng
    Yu, Lanfeng
    Yao, Xinsheng
    Tiedao Xuebao/Journal of the China Railway Society, 2000, 22 (03): : 33 - 37
  • [30] A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network
    He, Hu
    Zhang, Xiaoyong
    Jiang, Fu
    Wang, Chenglong
    Yang, Yingze
    Liu, Weirong
    Peng, Jun
    IFAC PAPERSONLINE, 2020, 53 (02): : 15374 - 15379
12345