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In this work we aim to predict the driver's focus of attention. The goal isto estimate what a person would pay attention to while driving, and which partof the scene around the vehicle is more critical for the task. To this end wepropose a new computer vision model based on a multi-branch deep architecturethat integrates three sources of information: raw video, motion and scenesemantics. We also introduce DR(eye)VE, the largest dataset of driving scenesfor which eye-tracking annotations are available. This dataset features morethan 500,000 registered frames, matching ego-centric views (from glasses wornby drivers) and car-centric views (from roof-mounted camera), further enrichedby other sensors measurements. Results highlight that several attentionpatterns are shared across drivers and can be reproduced to some extent. Theindication of which elements in the scene are likely to capture the driver'sattention may benefit several applications in the context of human-vehicleinteraction and driver attention analysis.

Predicting the Driver's Focus of Attention: the DR(eye)VE Project