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Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State
Joint Authors
Zhao, Shuan-Feng
Guo, Wei
Zhang, Chuan-wei
Source
Journal of Advanced Transportation
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving.
However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available.
The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake.
The driver usually adjusts his or her actions based on the observed road conditions.
Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data.
Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data.
The objective is to extract the effective component of the driving behavior information and to weaken the road information component.
Finally the effectiveness of the proposed method is verified by simulating driving experiments.
American Psychological Association (APA)
Zhao, Shuan-Feng& Guo, Wei& Zhang, Chuan-wei. 2017. Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1171116
Modern Language Association (MLA)
Zhao, Shuan-Feng…[et al.]. Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State. Journal of Advanced Transportation No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1171116
American Medical Association (AMA)
Zhao, Shuan-Feng& Guo, Wei& Zhang, Chuan-wei. Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1171116
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1171116