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    Abstract
2017 (Vol. 14, Issue: 2)
Research Article

Drivers' Fatigue Lane Departure Recognition

Gao Zhen-hai, Le Dinh Dat, Hu Hong-yu and Zhang Li-dan
State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun, 130022, China
 

DOI: 10.19026/rjaset.14.3990
Submitted Accepted Published
April ‎8, ‎2016 January 6, 2017 February 15, 2017

  How to Cite this Article:

Gao Zhen-hai, Le Dinh Dat, Hu Hong-yu and Zhang Li-dan, 2017. Drivers' Fatigue Lane Departure Recognition.  Research Journal of Applied Sciences, Engineering and Technology, 14(2): 61-66.

DOI: 10.19026/rjaset.14.3990

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.3990

Abstract:


In order to enrich the judgment index of the lane departure and avoid a sensitive system which is caused by missing vehicle signals, a method of detecting fatigue lane departure based on human-vehicle-road characteristics has been proposed. At first, an experiment about fatigue lane departure has been taken. And then, relevant parameters that can reveal the human-vehicle-road characteristics are collected and analyzed, compared with that under normal lane changing. At last fatigue lane departure recognition model is constructed based on Gaussian Mixture-Hidden Markov Model (GM-HMM). The recognition results show good performance under online and offline tests.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

    Copyright

© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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