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The task of driver attention prediction has drawn considerable interest amongresearchers in robotics and the autonomous vehicle industry. Driver attentionprediction can play an instrumental role in mitigating and preventing high-riskevents, like collisions and casualties. However, existing driver attentionprediction models neglect the distraction state and intention of the driver,which can significantly influence how they observe their surroundings. Toaddress 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 andintention of the driver. In addition, the attention data in our dataset iscaptured in both manual and autopilot modes using eye-tracking devices ofdifferent resolutions. Our results demonstrate that incorporating the above twodriver states into attention modeling can improve the performance of driverattention prediction. To the best of our knowledge, this work is the first toprovide autopilot attention data. Furthermore, CoCAtt is currently the largestand the most diverse driver attention dataset in terms of autonomy levels, eyetracker resolutions, and driving scenarios.

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset