  Human drivers use their attentional mechanisms to focus on critical objects
and make decisions while driving. As human attention can be revealed from gaze
data, capturing and analyzing gaze information has emerged in recent years to
benefit autonomous driving technology. Previous works in this context have
primarily aimed at predicting "where" human drivers look at and lack knowledge
of "what" objects drivers focus on. Our work bridges the gap between
pixel-level and object-level attention prediction. Specifically, we propose to
integrate an attention prediction module into a pretrained object detection
framework and predict the attention in a grid-based style. Furthermore,
critical objects are recognized based on predicted attended-to areas. We
evaluate our proposed method on two driver attention datasets, BDD-A and
DR(eye)VE. Our framework achieves competitive state-of-the-art performance in
the attention prediction on both pixel-level and object-level but is far more
efficient (75.3 GFLOPs less) in computation.
