  Despite the advent of autonomous cars, it's likely - at least in the near
future - that human attention will still maintain a central role as a guarantee
in terms of legal responsibility during the driving task. In this paper we
study the dynamics of the driver's gaze and use it as a proxy to understand
related attentional mechanisms. First, we build our analysis upon two
questions: where and what the driver is looking at? Second, we model the
driver's gaze by training a coarse-to-fine convolutional network on short
sequences extracted from the DR(eye)VE dataset. Experimental comparison against
different baselines reveal that the driver's gaze can indeed be learnt to some
extent, despite i) being highly subjective and ii) having only one driver's
gaze available for each sequence due to the irreproducibility of the scene.
Eventually, we advocate for a new assisted driving paradigm which suggests to
the driver, with no intervention, where she should focus her attention.
