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

本文旨在预测驾驶员的注意力集中情况，提出了一种基于多分支深度架构的计算机视觉模型，集成了三种信息源：原始视频、动态和场景语义，并介绍了 DR (eye) VE，这是迄今为止最大的驾驶场景数据集，结果强调了跨驾驶员共享的注意力模式，可在人车交互和驾驶员注意力分析等多个应用方面受益。