基于方向盘转角近似熵与复杂度的驾驶人疲劳状态识别

Driver fatigue recognition based on approximated entropy and complexity of steering wheel angle

提出了一种基于实车工况下方向盘转角近似熵与复杂度的驾驶人疲劳状态识别方法。利用动态时间序列的非线性特征构造理论,计算短时方向盘转角时间序列的近似熵(ApEn)与复杂度特征。以此非线性特征为输入,设计了硬阈值多级疲劳判别模型,构建了"二入二出"的疲劳等级映射规则,输出驾驶过程样本的疲劳预判状态。将近邻样本的疲劳预判状态进行比较,实现了驾驶人的三级疲劳状态识别。进行了实车实验。结果表明:本方法对三级疲劳状态识别的平均正确率达到84.6%;因而,本方法具有工程应用价值。

A novel approach was proposed to recognize driver fatigue status under real driving conditions based on approximate-entropy （ApEn） and complexity of steering wheel angle （SWA）. This approach first calculated a non-linear ApEn and complexity of SWA in a short time sliding window based on structural theory of dynamic time-series data analysis. Then a hard threshold based multi-level fatigue status detection model was designed to process the non-linear feature from first stage, and the pre-diagnosis of fatigue level was outputted from a two-input-two-output fatigue mapping rule based on observed samples. The pre-diagnosed fatigue statuses of the observed sample and its adjacent samples were compared to determine the three-level fatigue status of the observed sample. The experiment was set on real road driving environment. The results show that the approach performs an averaged accuracy of 84.6% for three-level fatigue detection on real road driving test. Therefore, the approach can be especially applied to several industrial applications.