Driver attention prediction implies the intention understanding of where the
driver intends to go and what object the driver concerned about, which commonly
provides a driving task-guided traffic scene understanding. Some recent works
explore driver attention prediction in critical or accident scenarios and find
a positive role in helping accident prediction, while the promotion ability is
constrained by the prediction accuracy of driver attention maps. In this work,
we explore the network connection gating mechanism for driver attention
prediction (Gate-DAP). Gate-DAP aims to learn the importance of different
spatial, temporal, and modality information in driving scenarios with various
road types, occasions, and light and weather conditions. The network connection
gating in Gate-DAP consists of a spatial encoding network gating,
long-short-term memory network gating, and information type gating modules.
Each connection gating operation is plug-and-play and can be flexibly
assembled, which makes the architecture of Gate-DAP transparent for evaluating
different spatial, temporal, and information types for driver attention
prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority
of the proposed method with the comparison with state-of-the-art approaches.
The code is available on this https URL

通过网络连接控制机制进行驾驶员注意力预测，提高预测准确性，并且在不同场景中验证了该方法的优越性。

门控驱动注意力预测器

Gated Driver Attention Predictor

The task of driver attention prediction has drawn considerable interest among
researchers in robotics and the autonomous vehicle industry. Driver attention
prediction can play an instrumental role in mitigating and preventing high-risk
events, like collisions and casualties. However, existing driver attention
prediction models neglect the distraction state and intention of the driver,
which can significantly influence how they observe their surroundings. To
address these issues, we present a new driver attention dataset, CoCAtt
(Cognitive-Conditioned Attention). Unlike previous driver attention datasets,
CoCAtt includes per-frame annotations that describe the distraction state and
intention of the driver. In addition, the attention data in our dataset is
captured in both manual and autopilot modes using eye-tracking devices of
different resolutions. Our results demonstrate that incorporating the above two
driver states into attention modeling can improve the performance of driver
attention prediction. To the best of our knowledge, this work is the first to
provide autopilot attention data. Furthermore, CoCAtt is currently the largest
and the most diverse driver attention dataset in terms of autonomy levels, eye
tracker resolutions, and driving scenarios. CoCAtt is available for download at
this https URL.

本研究提出了一个新的驾驶员注意力数据集，CoCAtt，通过使用眼动仪设备来捕捉手动和自动驾驶模式下的注意力数据，包含表述驾驶员分心状态和意图的每帧注释。结果表明，将驾驶状态和意图纳入注意力建模中可以提高驾驶员注意力预测的性能，并且 CoCAtt 是目前自主等级、眼动仪分辨率及驾驶情境最多元和最大的驾驶员注意力数据集。