Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Lingxiang Wei, Tianliu Feng, Pengfei Zhao, Mingjun Liao


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.


driver sleepiness; driving simulator; pattern recognition; relevance vector machine (RVM); traffic safety

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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.

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.

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.

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.

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.

Deng, W., & Wu, R. (2019). Real-Time Driver-Drowsiness Detection System Using Facial Features. IEEE Access, 7, 118727–118738.

Fang, L. (1993). Elementary operations and Laplace’s Theorem on Quantum Matrices. Journal of Physics A: Mathematical and General, 26(17), 4287–4297.

Gonçalves, M., Amici, R., Lucas, R., Åkerstedt, T., Cirignotta, F., Horne, J., et al. (2015a). Sleepiness at the Wheel Across Europe: A Survey of 19 Countries. Journal of Sleep Research, 24(3), 242–253.

Gonçalves, M., Peralta, A. R., Ferreira, J. M., & Guilleminault, C. (2015b). Sleepiness and Motor Vehicle Crashes in a Representative Sample of Portuguese Drivers: The Importance of Epidemiological Representative Surveys. Traffic Injury Prevention, 16(7), 677–683. 5389588.2015.1013535

He, Q., Li, W., Fan, X., & Fei, Z. (2015). Driver Fatigue Evaluation Model With Integration of Multi-Indicators Based on Dynamic Bayesian Network. IET Intelligent Transport Systems, 9(5), 547–554.

He, Q., Li, W., Fan, X., & Fei, Z. (2016). Evaluation of Driver Fatigue With Multi-Indicators Based on Artificial Neural Network. IET Intelligent Transport Systems, 10(8), 555–561.

Junaedi, S., & Akbar, H. (2018). Driver Drowsiness Detection Based on Face Feature and PERCLOS. Journal of Physics: Conference Series, 1090(1), 012037.

Jung, S. J., Shin, H. S., & Chung, W. Y. (2014). Driver Fatigue and Drowsiness Monitoring System With Embedded Electrocardiogram Sensor on Steering Wheel. IET Intelligent Transport Systems, 8(1), 43–50.

Kun, B., Lin, L., Li, Z., & Liang, F. S. (2012). 3D Face Recognition Method Based on Cascade Classifier. Procedia Engineering, 29, 705–709.

Mandal, B., Li, L., Wang, G. S., & Lin, J. (2017). Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State. IEEE Transactions on Intelligent Transportation Systems, 18(3), 545–557.

Martensson, H., Keelan, O., & Ahlstrom, C. (2018). Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving. IEEE Transactions on Intelligent Transportation Systems, 20(2), 421–430.

Ning, S., & Feng, Z. (2006). Analysis on the Cause of Road Traffic Accidents. Communications Standardization, 10, 152–155.

Osia, N., & Bourlai, T. (2014). A Spectral Independent Approach for Physiological and Geometric Based Face Recognition in the Visible, Middle-Wave and Long-Wave Infrared Bands. Image and Vision Computing, 32(11), 847–859.

Osuna, E., Freund, R., & Girosit, F. (1997). Training Support Vector Machines: An Application to Face Detection. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 31(5), 15–18.

Park, J. (2011). Plastic Optical Fiber Sensor for Measuring Driver-Gripping Force. Optical Engineering, 50(2), 020501.

Rehman, S. U., Tu, S., Huang, Y., & Yang, Z. (2016). Face Recognition: A Novel Un-Supervised Convolutional Neural Network Method. In IEEE International Conference of Online Analysis and Computing Science.

Ronen, A., Oron-Gilad, T., & Gershon, P. (2014). The Combination of Short Rest and Energy Drink Consumption as Fatigue Countermeasures During a Prolonged Drive of Professional Truck Drivers. Journal of Safety Research, 49, 39.e1–43.

Sun, W., Zhang, X., Peeta, S., He, X., & Li, Y. (2017). A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels. IEEE Transactions on Intelligent Transportation Systems, 18(12), 3408–3420.

Tian, L., & Ji, Q. (2019). Study on Fatigue Driving Test Based on Eye Information Fusion. The key magazine of China technology, 38(10), 26–29.

Wang, X., & Zhou, G. (2006). A Research on the SVM Method for Facial Recognition. Journal of Shanghai Institute of Technology, 6(2), 104–107.

Wang, Y., Ma, C., & Li, Y. (2018). Effects of Prolonged Tasks and Rest Patterns on Driver’s Visual Behaviors, Driving Performance, and Sleepiness Awareness in Tunnel Environments: A Simulator Study. Iranian Journal of Science & Technology Transactions of Civil Engineering, 42(2), 143–151.

Wang, Y., Ma, J., & Wei, L. (2019a). Investigating the Effect of Long Trip on Driving Performance, Eye Blinks, and Awareness of Sleepiness Among Commercial Drivers: A Naturalistic Driving Test Study. Scientia Iranica, 26(1), 95–102.

Wang, Z., Wang, X., & Li, G. (2019b). Face Recognition Based on Fusion of Eigen Face and Gray-Scale Transformation. Journal of Chinese Computer Systems, 40(2), 420–426.

Xu, C., Pei, S., & Wang, X. (2016). Driver Drowsiness Detection Based on Non-Intrusive Metrics Considering Individual Difference. China Journal of Highway and Transport, 29(10), 118–125.

Yan, C., Coenen, F., & Zhang, B. (2015). Driving Posture Recognition by Convolutional Neural Networks. IET Computer Vision, 10(2), 103–114.

Yeo, M. V. M., Li, X., Shen, K., & Wilder-Smith, E. P. V. (2009). Can SVM be Used for Automatic EEG Detection of Drowsiness During Car Driving? Safety Science, 47(1), 115–124.

Zhao, L., Wang, Z., Wang, X., & Liu, Q. (2018a). Driver Drowsiness Detection Using Facial Dynamic Fusion Information and a DBN. IET Intelligent Transport Systems, 12(2), 127–133.

Zhao, Y., Xu, G., Sun, Y., Pan, B., & Li, T. (2018b). A Portable High-Density Absolute-Measure NIRS Imager for Detecting Prefrontal Lobe Activity Under Fatigue Driving. Microelectronics Reliability, 82, 197–203.

DOI: 10.7250/bjrbe.2021-16.518


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