Driver fatigue Detection using Approximate Entropic of steering wheel angle from Real driving Data

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 intervalpercentage, 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.