Open AccessEvaluation of driver fatigue with multi‐indicators based on artificial neural network
Author(s) -
He Qichang,
Li Wei,
Fan Xiumin,
Fei Zhimin
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2015.0021
Subject(s) - artificial neural network , computer science , artificial intelligence , automotive engineering , engineering , transport engineering
Three kinds of signals include head nodding movement, eyelid closure and driving time are demonstrated to be the factors to indicate driver fatigue. On the basis of these factors, five fatigue‐based indicators are proposed in this study, which are integrated to establish the evaluation model of driver fatigue with artificial neural network. A total of 50 drivers are recruited to take part in the fatigue‐oriented experiments on a driving simulator. The electroencephalogram (EEG), head nodding angle, eye‐tracking signal, driving time‐of‐day and time‐on‐task are sampled during experiments. The EEG‐based indicator is determined and used to group the sample data into alert and drowsy. The head nodding‐based indicator includes mean value and dominant frequency of head nodding angle is proposed. The results show that the performance of the proposed evaluation model is better than that with sole head nodding‐based or eyelid closure‐based indicator.