A Distracted Driving Discrimination Method Based on the Facial Feature Triangle and Bayesian Network

Tianliu Feng, Lingxiang Wei, Wenjuan E, Pengfei Zhao, Zhe Li, Yuchuan Ji


Distracted driving is one of the main causes of road crashes. Therefore, effective distinguishing of distracted driving behaviour and its category is the key to reducing the incidence of road crashes. To identify distracted driving behaviour accurately and effectively, this paper uses the head posture as a relevant variable and realizes the classification of distracted driving behaviour based on the relevant literature and investigation. Adistracted driving discrimination algorithm based on the facial feature triangle is proposed. In the proposed algorithm, the Bayesian network is employed to judge driving behaviour categories. The proposed algorithm is verified by experiments using data from 20 volunteers. The experimental results show that the discrimination accuracy of the proposed algorithm is as high as 90%, which indicates that the head posture parameters used in this study are closely related to the distracted driving state. The results show that the proposed algorithm achieves high accuracy in the discrimination and classification of distracted driving behaviour and can effectively reduce the accident rate caused by distracted driving. Moreover, it can provide a basis for the research of distracted driving behaviour and is conducive to the formulation of the corresponding laws and regulations. 


Bayesian network; distracted driving behaviour; facial feature triangle; feature point recognition; head posture; traffic safety

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