Extraction Method of Driver's Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State
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 one is 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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
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Supplemental Notes:
- © 2017 Shuan-Feng Zhao et al.
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Authors:
- Zhao, S F
- Guo, W
- Zhang, C W
- Publication Date: 2017
Language
- English
Media Info
- Media Type: Web
- Features: References;
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Serial:
- Journal of Advanced Transportation
- Volume: 2017
- Issue Number: Article ID 9509213
- Publisher: John Wiley & Sons, Incorporated
- ISSN: 0197-6729
- EISSN: 2042-3195
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Automatic data collection systems; Behavior; Braking; Driver monitoring; Drivers; Fatigue (Physiological condition); Steering
- Subject Areas: Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01664739
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 29 2018 9:32AM