IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/385716.html
   My bibliography  Save this article

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

Author

Listed:
  • Mingheng Zhang
  • Gang Longhui
  • Zhe Wang
  • Xiaoming Xu
  • Baozhen Yao
  • Liping Zhou

Abstract

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.

Suggested Citation

  • Mingheng Zhang & Gang Longhui & Zhe Wang & Xiaoming Xu & Baozhen Yao & Liping Zhou, 2014. "Hybrid Model for Early Onset Prediction of Driver Fatigue with Observable Cues," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, July.
    • Handle: RePEc:hin:jnlmpe:385716
    DOI: 10.1155/2014/385716
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2014/385716.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2014/385716.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2014/385716?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:385716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.