基于相关向量机的驾驶员瞌睡检测算法

IF 0.6 4区 工程技术 Q4 ENGINEERING, CIVIL Baltic Journal of Road and Bridge Engineering Pub Date : 2021-03-29 DOI:10.7250/BJRBE.2021-16.518
Lingxiang Wei, Tianliu Feng, Pengfei Zhao, M-L. Liao
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引用次数: 3

摘要

驾驶员嗜睡是造成交通事故的最重要原因之一。有效和稳定的算法对于区分非疲劳状态和疲劳状态至关重要。相关向量机(RVM)作为一种前沿检测方法,可以满足这一要求,并代表了疲劳状态检测的潜在解决方案。为了准确有效地识别驾驶员的疲劳状态,减少因驾驶员嗜睡引起的交通事故数量,本文将驾驶员的张开嘴和眼睛状态视为多源相关变量,并在相关文献和调查的基础上,建立了疲劳和非疲劳状态的分类。在此基础上,提出了一种用于疲劳状态自动检测的RVM模型。20名男性受访者参与了数据收集过程,共获得了1000个驾驶状态数据集(一半为非疲劳,一半为疲劳)。用不同的RVM分类器对疲劳状态识别结果进行了分析。结果表明,具有不同核函数的RVM驱动状态分类器的识别准确率高于90%,这表明本工作中使用的张开度和眼睛状态指数与疲劳状态密切相关。基于所获得的结果,所提出的疲劳状态识别方法具有提高疲劳状态检测精度的潜力。更重要的是,它为疲劳状态预警方法的发展提供了科学的理论依据。
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Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine
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.
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来源期刊
Baltic Journal of Road and Bridge Engineering
Baltic Journal of Road and Bridge Engineering 工程技术-工程:土木
CiteScore
2.10
自引率
9.10%
发文量
25
审稿时长
>12 weeks
期刊介绍: THE JOURNAL IS DESIGNED FOR PUBLISHING PAPERS CONCERNING THE FOLLOWING AREAS OF RESEARCH: road and bridge research and design, road construction materials and technologies, bridge construction materials and technologies, road and bridge repair, road and bridge maintenance, traffic safety, road and bridge information technologies, environmental issues, road climatology, low-volume roads, normative documentation, quality management and assurance, road infrastructure and its assessment, asset management, road and bridge construction financing, specialist pre-service and in-service training;
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