Understanding what drivers look at is important for many applications,
including driver training, monitoring, and assistance, as well as self-driving.
Traditionally, factors affecting human visual attention have been divided into
bottom-up (involuntary attraction to salient regions) and top-down (task- and
context-driven). Although both play a role in drivers' gaze allocation, most of
the existing modeling approaches apply techniques developed for bottom-up
saliency and do not consider task and context influences explicitly. Likewise,
common driving attention benchmarks lack relevant task and context annotations.
Therefore, to enable analysis and modeling of these factors for drivers' gaze
prediction, we propose the following: 1) address some shortcomings of the
popular DR(eye)VE dataset and extend it with per-frame annotations for driving
task and context; 2) benchmark a number of baseline and SOTA models for
saliency and driver gaze prediction and analyze them w.r.t. the new
annotations; and finally, 3) a novel model that modulates drivers' gaze
prediction with explicit action and context information, and as a result
significantly improves SOTA performance on DR(eye)VE overall (by 24\% KLD and
89\% NSS) and on a subset of action and safety-critical intersection scenarios
(by 10--30\% KLD). Extended annotations, code for model and evaluation will be
made publicly available.

驾驶员注意力、注视预测、任务和环境、显著性和 DR (eye) VE 数据集是该研究的主要关键词和研究领域。

理解和建模任务与环境对驾驶员注视分配的影响

Understanding and Modeling the Effects of Task and Context on Drivers'  Gaze Allocation

Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aware of this problem and collected the driver attention in
laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses
the critical situations which do not encounter actual accidents, and just faces
the driver attention prediction task, without a close step for accident
prediction. In contrast to this, we explore the view of drivers' eyes for
capturing multiple kinds of accidents, and construct a more diverse and larger
video benchmark than ever before with the driver attention and the driving
accident annotation simultaneously (named as DADA-2000), which has 2000 video
clips owning about 658,476 frames on 54 kinds of accidents. These clips are
crowd-sourced and captured in various occasions (highway, urban, rural, and
tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and
nighttime). For the driver attention representation, we collect the maps of
fixations, saccade scan path and focusing time. The accidents are annotated by
their categories, the accident window in clips and spatial locations of the
crash-objects. Based on the analysis, we obtain a quantitative and positive
answer for the question in this paper.

本文提出了一个新的基于驾驶员目光追踪和车祸注释的视频基准测试集（DADA-2000），涵盖了 54 种不同类型的车祸，可以更全面地预测即将发生的事故。

DADA-2000: 驾驶员专注度能否预测驾驶事故？通过基准测试分析

DADA-2000: Can Driving Accident be Predicted by Driver Attention?  Analyzed by A Benchmark

Robust driver attention prediction for critical situations is a challenging
computer vision problem, yet essential for autonomous driving. Because critical
driving moments are so rare, collecting enough data for these situations is
difficult with the conventional in-car data collection protocol---tracking eye
movements during driving. Here, we first propose a new in-lab driver attention
collection protocol and introduce a new driver attention dataset, Berkeley
DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos
selected from a large-scale, crowd-sourced driving video dataset. We further
propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to
identify crucial frames of a driving dataset and weights them heavily during
model training. With our dataset and HWS, we built a driver attention
prediction model that outperforms the state-of-the-art and demonstrates
sophisticated behaviors, like attending to crossing pedestrians but not giving
false alarms to pedestrians safely walking on the sidewalk. Its prediction
results are nearly indistinguishable from ground-truth to humans. Although only
being trained with our in-lab attention data, the model also predicts in-car
driver attention data of routine driving with state-of-the-art accuracy. This
result not only demonstrates the performance of our model but also proves the
validity and usefulness of our dataset and data collection protocol.

提出一种新的驾驶员注意力数据收集协议并介绍了一个新的驾驶员注意力数据集 BDD-A，进一步提出了人工加权采样（HWS）方法来构建驾驶员注意力预测模型，该模型不仅表现出比现有模型更好的性能，而且还是基于常规驾驶过程中的实际数据预测的。

在关键情况下预测驾驶员的注意力

Predicting Driver Attention in Critical Situations

Fast and accurate upper-body and head pose estimation is a key task for
automatic monitoring of driver attention, a challenging context characterized
by severe illumination changes, occlusions and extreme poses. In this work, we
present a new deep learning framework for head localization and pose estimation
on depth images. The core of the proposal is a regression neural network,
called POSEidon, which is composed of three independent convolutional nets
followed by a fusion layer, specially conceived for understanding the pose by
depth. In addition, to recover the intrinsic value of face appearance for
understanding head position and orientation, we propose a new Face-from-Depth
approach for learning image faces from depth. Results in face reconstruction
are qualitatively impressive. We test the proposed framework on two public
datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new
challenging dataset mainly inspired by the automotive setup. Results show that
our method overcomes all recent state-of-art works, running in real time at
more than 30 frames per second.

本文提出了一种基于深度图像的头部姿态估计的新型深度学习框架 POSEidon，包括三个卷积网络和一个融合层，同时还提出了一种基于深度学习的人脸表征方法用于理解头部的位置和方向，实验结果表明该方法在实时监测司机关注度方面具有很好的性能和应用潜力。