Driver drowsiness detection with eyelid related parameters by Support Vector Machine

Authors:
Shuyan Hu;Gangtie Zheng
Affiliations:
School of Astronautics, Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing 100083, China;School of Astronautics, Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing 100083, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness levels, and then a paired t-test is done to identify how the parameters are associated with drivers' sleepy condition. With all the features, a SVM drowsiness detection model is constructed. The validation results show that the drowsiness detection accuracy is quite high especially when the subjects are very sleepy.