NDLI: A novel driver fatigue assessment in uncertain traffic condition

Content Provider	IEEE Xplore Digital Library
Author	Guo Wenqiang Xiao Qinkun Hou Yongyan Zhang Baorong Peng Cheng
Copyright Year	2014
Description	Author affiliation: Coll. of Electr. & Inf. Eng., Shaanxi Univ. of Sci. & Technol., Xi'an, China (Guo Wenqiang; Hou Yongyan; Zhang Baorong; Peng Cheng) \|\| Coll. of Electron. & Inf., Xi'an Technol. Univ., Xi'an, China (Xiao Qinkun)
Abstract	In order to assess the driver fatigue in the dynamic, noisy and uncertain traffic conditions, this paper proposes a driver fatigue assessment system with a Bayesian network (BN). The multiple source feature data, such as percent eye closure and other behaviors that characterize a driver's level of fatigue, sampled from driving subsystems, are processed into training and testing data sets. Using the training data, the assessment BN is modeled, and then testing features data sets presented to the assessment BN model to detect the onset of driver fatigue. By existing BN inference algorithms, and the inference result for driver fatigue assessment is provided. The presented approach achieves the assessment with not only complete evidences but also incomplete ones. Experimental results show that the proposed approach is more effective and robust in bringing out the driver fatigue classification than the traditional Radius basis function neural network method.
Starting Page	4777
Ending Page	4781
File Size	95408
Page Count	5
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6895747
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	inference Eyelids fatigue behaviors Fatigue Educational institutions Data models Inference algorithms Situation assessment Bayes methods Bayesian network Vehicles
Content Type	Text
Resource Type	Article

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