Zuojin Li, Shengbo E. Li, Renjie Li, Bo Cheng, and Jinliang Shi
Driver fatigue, steering wheel angle (SWA), approximate entropy(ApEn), fatigue detection
This paper presents a steering-wheel-angle-based driver fatigue detection method for real driving conditions. This method extracts approximate entropy (ApEn) feature from recorded steering wheel angle (SWA) signal with a decision-tree-like classifier to identify the driving fatigue level. ApEn is extracted from fixed-size sliding window on real-time SWA series. To further exploit the in-depth information of SWA, additional features including interval- percentage, deviation, kurtosis and complexity value of ApEn are extracted and applied to the designed classifier. The experiment is set on 14.68 h of real road driving, the collected data has been segmented into three fatigue levels (“awake , “drowsy , “very drowsy ). The classification result showed that the proposed method achieves an averaged accuracy of 82.07%. These results confirm that the proposed method is effective in the detection of real-time driver fatigue.
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