Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine
-
Published:2021-03-29
Issue:1
Volume:16
Page:118-139
-
ISSN:1822-427X
-
Container-title:The Baltic Journal of Road and Bridge Engineering
-
language:
-
Short-container-title:BJRBE
Author:
Wei Lingxiang1ORCID, Feng Tianliu1ORCID, Zhao Pengfei2ORCID, Liao Mingjun3ORCID
Affiliation:
1. Yancheng Institute of Technology 2. Beijing University of Civil Engineering and Architecture 3. Yancheng Institute of Technology; Beijing Jiaotong University
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.
Publisher
Riga Technical University
Subject
Building and Construction,Civil and Structural Engineering
Reference33 articles.
1. Aghaei, A. S., Donmez, B., Liu, C. C., He, D., Liu, G., Plataniotis, K. N., Chen, H.-Y. W., Sojoudi, Z. (2016). Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the Driver’s Seat. IEEE Signal Processing Magazine, 33(6), 35–48. https://doi.org/10.1109/msp.2016.2602379 2. Ahlstrom, C., Nyström, M., Holmqvist, K., Fors, C., Sandberg, D., Anund, A., Kecklund, G., & Åkerstedt, T. (2013). Fit-for-Duty Test for Estimation of Drivers’ Sleepiness Level: Eye Movements Improve the Sleep/Wake Predictor. Transportation Research Part C Emerging Technologies, 26, 20–32. https://doi.org/10.1016/j.trc.2012.07.008 3. Baronti, F., Lenzi, F., Roncella, R., & Saletti, R. (2009). Distributed Sensor for Steering Wheel Grip Force Measurement in Driver Fatigue Detection. In Design, Automation & Test in Europe Conference & Exhibition. https://doi.org/10.1109/date.2009.5090790 4. Caesarendra, W., Widodo, A., & Yang, B.-S. (2010). Application of Relevance Vector Machine and Logistic Regression for Machine Degradation Assessment. Mechanical Systems and Signal Processing, 24(4), 1161–1171. https://doi.org/10.1016/j.ymssp.2009.10.011 5. Chai, X. J., Shan, S. G., Qing, L. Y., Chen, X., & Gao, W. (2006). Pose and Illumination Invariant Face Recognition Based on 3D Face Reconstruction. Journal of Software, 17(3), 525–534. https://doi.org/10.1360/jos170525
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|