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

Zuojin Li · Shengbo Eben Li · Renjie Li · Bo Cheng · Jinliang Shi

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Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Non-Invasive Vital Sign Monitoring

Abstract

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.

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References

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2022Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles 2023Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review 2020Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges 2020Multimodal Features for Detection of Driver Stress and Fatigue: Review

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