Abstract
Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.
Highlights
Driver sleepiness is one of the main causes of traffic accidents, which severely threatens road traffic safety
The results show that the recognition accuracy of the relevance vector machine (RVM)-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state
In order to identify the driver’s fatigue state accurately and efficiently, this paper proposes a multi-index fusion discrimination algorithm based on face recognition
Summary
Driver sleepiness is one of the main causes of traffic accidents, which severely threatens road traffic safety. Among human driving behaviors, driver sleepiness is most difficult to detect even though it has a high incidence rate. The driver’s fatigue state detection becomes a hotspot in the driver sleepiness. The driver sleepiness detection methods can be roughly divided into subjective and objective. The non-intrusive detection mainly detects the fatigue state by detecting the facial indicators, including eyes and mouth. The non-intrusive detection methods can detect the fatigue state without affecting the driver’s driving. In order to identify the driver’s fatigue state accurately and efficiently, this paper proposes a multi-index fusion discrimination algorithm based on face recognition.
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