小波能量熵在疲劳驾驶检测中的应用

用转向盘转向小波能量熵的分布来衡量转向操作的不平稳性,不涉及转向具体角度,受道路线形影响小,因此理论上采用转向熵的驾驶疲劳检测方法比采用具体转向值具有更高的精度.在此之前需确定转向熵与驾驶疲劳之间的关系.模拟驾驶实验表明,转向小波能量熵与疲劳程度之间存在正相关关系.首先去除转向盘转向信号中道路线形影响,然后利用Daubechies小波对其进行分解,以200 s为信号采样长度计算转向信号沿第5尺度的小波能量熵分布,并利用平滑修正窗修正偶然因素对能量熵分布的影响,发现随着疲劳的加深,转向能量熵呈上升趋势.对实验样本分析表明,驾驶员在疲劳发展过程中能量熵变化范围为0.05～0.24:最大值在0.16～0.24之间,最小值在0.05～0.11之间.驾驶人进入深度疲劳时转向小波能量熵比刚刚出现疲劳迹象时要增长50％～60％.     

面向驾驶人疲劳检测的人脸视频分析算法研究

疲劳驾驶是造成交通事故的重要原因.基于面部视频分析技术,对驾驶人的眼睛动作和状态进行特征分析,可以有效估计驾驶人的疲劳状态,但驾驶过程中驾驶人面部姿态和光照条件的变化使眼睛的准确定位变得困难.本文以主动形状模型(ASM,Active Shape Model)为基础对面部区域进行配准,结合Lucas-Kanade光流算法进行全局跟踪,并采用基于自商图的Meanshift算法进行局部校准.实验结果表明,Meanshift算法能够有效消除光流全局跟踪中的累积误差,有效提高人眼定位的精度.     

Using kinematic driving data to detect sleep apnea treatment adherence

People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea (OSA). We collected driving data and medication adherence from a sample of 75 OSA patients over 3.5 months. We converted speed and acceleration behaviors to symbols using symbolic aggregate approximation and converted these symbols to pattern frequencies using a sliding window. The resulting frequency data was matched with treatment adherence information. A random forest model was trained on the data and evaluated using a held-aside test dataset. The random forest model detects lapses in treatment adherence. An assessment of variable importance suggests that the important patterns of driving in classification correspond to route decisions and patterns that may be associated with drowsy driving. The success of this approach suggests driving data may be valuable for evaluating new treatments, analyzing side effects of medications, and that the approach may benefit other drowsiness detection algorithms.