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

机译：使用方向盘角度实时检测驾驶员疲劳状况

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摘要

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) features from fixed sliding windows on real-time steering wheel angles time series. After that, 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.
机译：本文基于从安装在方向盘上的传感器收集的方向盘角度（SWA）数据，提出了一种睡意在线检测系统，用于监测实际驾驶条件下的驾驶员疲劳水平。该系统首先在实时方向盘角度时间序列上从固定的滑动窗口中提取近似熵（ApEn）特征。之后，该系统通过使用给定偏差的自适应分段线性拟合线性化ApEn功能序列。然后，检测系统计算样本数据的线性特征序列之间的翘曲距离。最后，该系统根据设计的二元决策分类器，使用翘曲距离确定驾驶员的睡意状态。实验数据是在实际道路条件下从14.68 h行驶中收集的，包括两个疲劳级别：“唤醒”和“昏昏欲睡”。结果表明，所提出的系统能够以平均78.01％的准确度，29.35％的“清醒”状态错误检测和15.15％的“昏昏欲睡”状态错误检测功能进行在线工作。结果还证实，基于SWA信号的建议方法对于防止驾驶员疲劳引起的交通事故具有重要的应用价值。

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