A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network
Joint Authors
Jin, Yi
Liu, BingYou
Yang, HuiCheng
Zhu, WenBo
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-05
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving.
This method measures driving states using multifeature fusion.
First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function.
Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists.
Finally, the recognition result is given according to the combination of revised evidence based on Dempster’s rule.
Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the combination of information given by multiple features.
The proposed method can also effectively and accurately describe driving states.
American Psychological Association (APA)
Zhu, WenBo& Yang, HuiCheng& Jin, Yi& Liu, BingYou. 2017. A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1191265
Modern Language Association (MLA)
Zhu, WenBo…[et al.]. A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network. Mathematical Problems in Engineering No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1191265
American Medical Association (AMA)
Zhu, WenBo& Yang, HuiCheng& Jin, Yi& Liu, BingYou. A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1191265
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191265