Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver's visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver's attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver's visual behavior in terms of computer vision to estimate the driver's attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver's attention locations.

中文翻译：

---

用于驾驶员视觉注意力预测的高分辨率神经网络。

驾驶是一项对视觉信息有严格要求的任务，因此人类视觉系统在为安全驾驶做出正确决策方面起着至关重要的作用。在高级驾驶员辅助系统（ADAS）和高效自动驾驶汽车（AV）中，了解驾驶员的视觉注意力和相关行为信息是一项具有挑战性但必不可少的任务。具体来说，从图像中可靠地预测驾驶员的注意力可能是协助需要自动驾驶汽车安全行驶以与周围环境互动的智能车辆系统的关键关键。因此，在本文中，我们根据计算机视觉研究了驾驶员的视觉行为，以估计驾驶员在图像中的注意位置。第一，我们显示，高分辨率特征表示与低分辨率特征融合后，可以提高视觉注意力预测的准确性和定位性能。为了证明这一点，我们采用了深度卷积神经网络框架，该框架可以在多种分辨率下学习和提取特征表示。特别地，网络以原始图像分辨率保持最高分辨率的特征表示。其次，当使用典型的视觉注意力数据集训练神经网络时，注意力预测倾向于偏向图像中心。为了避免过度适合中心偏见的解决方案，使用图像的不同区域来训练网络。最后，实验结果验证了我们提出的框架提高了驾驶员注意位置的预测准确性。