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.

本文介绍了两个嵌入在驾驶模拟器中的凝视区域估计模块，一个使用 3D Kinect 设备，另一个使用虚拟现实 Oculus Rift 设备。通过实验发现，Oculus Rift 是用于凝视估计的最佳硬件，其凝视区域估计方法表现最佳，准确度达到 97.94%。这一凝视信息丰富了驾驶模拟器数据，使得除了提供虚拟现实体验外，还能实现多模态驾驶表现分析。

驾驶模拟器中基于 Kinect 和 Oculus 的凝视区域估计方法的比较分析

Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region  Estimation Methods in a Driving Simulator

Inspired by the finding that vanishing point (road tangent) guides driver's
gaze, in our previous work we showed that vanishing point attracts gaze during
free viewing of natural scenes as well as in visual search (Borji et al.,
Journal of Vision 2016). We have also introduced improved saliency models using
vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict
vanishing points in naturalistic environments by training convolutional neural
networks in an end-to-end manner over a large set of road images downloaded
from Youtube with vanishing points annotated. Results demonstrate effectiveness
of our approach compared to classic approaches of vanishing point detection in
the literature.

通过训练卷积神经网络，我们针对自然场景中的路面图像进行了消失点预测，并与文献中经典的消失点检测方法进行了比较。结果显示，我们的方法能够更有效地预测消失点。