论文标题
基于热图的方法来估计驱动程序的认知分散注意力
Heatmap-Based Method for Estimating Drivers' Cognitive Distraction
论文作者
论文摘要
为了提高道路安全性，在视觉和手动分散注意力中，现代智能车辆还需要检测认知分心的驾驶（即驾驶员的思维徘徊）。在这项研究中，探索了认知过程对驾驶员凝视行为的影响。提出了一种基于图像的新型表示眼光分散体的表示，以估计认知分散注意力。数据是在开放的高速公路上收集的，该道路具有量身定制的协议，以产生认知分散注意力。创建形状的视觉差异表明，与分心的驾驶相比，驾驶员探索了中性驾驶中更广阔的区域。因此，训练了支持向量机（SVM）的分类器，即使有一个小数据集，也可以针对两级问题达到85.2％的精度。因此，所提出的方法具有判别能力，可以使用凝视信息识别认知分散注意力。最后，这项工作详细介绍了这种基于图像的表示如何对其他分心驾驶检测的情况有用。
In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the drivers mind wandering). In this study, the influence of cognitive processes on the drivers gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral driving compared to distracted driving. Thus, support vector machine (SVM)-based classifiers are trained, and 85.2% of accuracy is achieved for a two-class problem, even with a small dataset. Thus, the proposed method has the discriminative power to recognize cognitive distraction using gaze information. Finally, this work details how this image-based representation could be useful for other cases of distracted driving detection.
