Understanding what drivers look at is important for many applications,
including driver training, monitoring, and assistance, as well as self-driving.
Traditionally, factors affecting human visual attention have been divided into
bottom-up (involuntary attraction to salient regions) and top-down (task- and
context-driven). Although both play a role in drivers' gaze allocation, most of
the existing modeling approaches apply techniques developed for bottom-up
saliency and do not consider task and context influences explicitly. Likewise,
common driving attention benchmarks lack relevant task and context annotations.
Therefore, to enable analysis and modeling of these factors for drivers' gaze
prediction, we propose the following: 1) address some shortcomings of the
popular DR(eye)VE dataset and extend it with per-frame annotations for driving
task and context; 2) benchmark a number of baseline and SOTA models for
saliency and driver gaze prediction and analyze them w.r.t. the new
annotations; and finally, 3) a novel model that modulates drivers' gaze
prediction with explicit action and context information, and as a result
significantly improves SOTA performance on DR(eye)VE overall (by 24\% KLD and
89\% NSS) and on a subset of action and safety-critical intersection scenarios
(by 10--30\% KLD). Extended annotations, code for model and evaluation will be
made publicly available.

驾驶员注意力、注视预测、任务和环境、显著性和 DR (eye) VE 数据集是该研究的主要关键词和研究领域。