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Sensors 2017, 17(3), 495;

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
State Key Lab of Automotive Safety and Energy, Department of Automotive Engineering, Tsinghua University, Beijing 100084, China
Authors to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 1 January 2017 / Revised: 8 February 2017 / Accepted: 28 February 2017 / Published: 2 March 2017
(This article belongs to the Special Issue Sensors for Transportation)
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This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue. View Full-Text
Keywords: steering wheel angles (SWA); approximate entropy (ApEn); warping distance; fatigue detection steering wheel angles (SWA); approximate entropy (ApEn); warping distance; fatigue detection

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Li, Z.; Li, S.E.; Li, R.; Cheng, B.; Shi, J. Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions. Sensors 2017, 17, 495.

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