CoCAtt: A Cognitive-Conditioned Driver Attention Dataset
- URL: http://arxiv.org/abs/2111.10014v2
- Date: Tue, 23 Nov 2021 17:06:08 GMT
- Title: CoCAtt: A Cognitive-Conditioned Driver Attention Dataset
- Authors: Yuan Shen and Niviru Wijayaratne and Pranav Sriram and Aamir Hasan and
Peter Du and Katie Driggs-Campbell
- Abstract summary: Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events.
We present a new driver attention dataset, CoCAtt.
CoCAtt is the largest and the most diverse driver attention dataset in terms of autonomy levels, eye tracker resolutions, and driving scenarios.
- Score: 16.177399201198636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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