Academic Journal
Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine
| العنوان: | Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine |
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| المؤلفون: | Lingxiang Wei, Tianliu Feng, Pengfei Zhao, Mingjun Liao |
| المصدر: | The Baltic Journal of Road and Bridge Engineering, Vol 16, Iss 1, Pp 118-139 (2021) |
| بيانات النشر: | Riga Technical University Press, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | driver sleepiness, driving simulator, pattern recognition, relevance vector machine (rvm), traffic safety, Highway engineering. Roads and pavements, TE1-450, Bridge engineering, TG1-470 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1822-427X 1822-4288 |
| Relation: | https://bjrbe-journals.rtu.lv/article/view/4644; https://doaj.org/toc/1822-427X; https://doaj.org/toc/1822-4288 |
| DOI: | 10.7250/bjrbe.2021-16.518 |
| URL الوصول: | https://doaj.org/article/3519c5930d7c49e586ea57ea587be9eb |
| رقم الانضمام: | edsdoj.3519c5930d7c49e586ea57ea587be9eb |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 1822427X 18224288 |
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| DOI: | 10.7250/bjrbe.2021-16.518 |