Evaluation of driver fatigue with multi‐indicators based on artificial neural network. Issue 8 (1st October 2016)

• Record Type: Journal Article
• Authors: He, Qichang; Li, Wei; Fan, Xiumin; Fei, Zhimin
• Is Part Of: IET intelligent transport systems. Volume 10:Issue 8(2016)
• Page Start: 555
• Page End: 561
• Publication Date: 2016-10-01
• Subjects: electroencephalography -- neural nets -- medical signal processing -- driver information systems; artificial neural network -- head nodding movement -- eyelid closure -- fatigue‐based indicators -- electroencephalogram -- eye‐tracking signal -- EEG‐based indicator; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2015.0021
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.