Abstract
Driver's gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers' gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.
Abstract (translated)
驾驶员的目光信息对于驾驶研究至关重要，因为它与驾驶员注意力的关系。特别是，将目光数据纳入驾驶模拟器中扩大了研究研究的范围，因为它们可以关系驾驶员目光模式及其特征和表现。在本文中，我们介绍了一个驾驶模拟器中的两个目光区域估计模块。一个使用Kinect 3D设备，另一个使用Oculus Rift虚拟现实设备。这些模块能够检测出在处理每个路线的帧中，驾驶员在目光中所视的区域。我们比较了四种目光估计方法，它们学习了目光移动与头部移动之间的关系。两种方法更简单，基于试图捕捉这种关系的点，而两种方法基于像MLP和SVM这样的分类器。我们对12名用户在同一场景中进行了实验，每个人分别使用不同的可视化显示，先用大屏幕，然后使用Oculus Rift。总的来说，Oculus Rift超越了Kinect，成为目光估计的最佳硬件。基于Oculus的眼光区域估计方法达到97.94%的准确度。Oculus Rift模块提供的信息使驾驶模拟器数据更加丰富，使其能够进行多模态驾驶性能分析，而不仅仅是通过Oculus提供的虚拟现实体验获得的沉浸和现实感。
URL
https://arxiv.org/abs/2402.05248
