Analysis of driver fatigue causations based on the Bayesian network model. (July 2017)

• Record Type: Journal Article
• Authors: Gang, Longhui; Song, Xiaolin; Zhang, Mingheng; Yao, Baozhen; Zhou, Liping
• Is Part Of: Simulation. Volume 93:Number 7(2017)
• Page Start: 553
• Page End: 565
• Publication Date: 2017-07
• Subjects: Driver fatigue -- Bayesian network -- maximum likelihood estimation -- junction tree; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549716665712
• Languages: English
• ISSNs: 0037-5497

Abstract

Driver fatigue is the major reason for severe traffic accidents. At present, the driver's driving state evaluation, based on multi-source information fusion, has become a hotspot in the research field of vehicle safety assistant driving. The purpose of this paper is to build a Bayesian network model for driver fatigue causation analysis considering several visual cues, such as Percentage of Eyelid Closure over the Pupil over Time, Average Eye Closure Speed, etc. The proposed method was divided into three stages, that is, variables analysis, model structure design, and model parameter determination. Finally, the presented model and algorithm were illustrated with a simulation experiment and conclusions were inferred from the experiment data analysis.