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。