  Predicting attention regions of interest is an important yet challenging task
for self-driving systems. Existing methodologies rely on large-scale labeled
traffic datasets that are labor-intensive to obtain. Besides, the huge domain
gap between natural scenes and traffic scenes in current datasets also limits
the potential for model training. To address these challenges, we are the first
to introduce an unsupervised way to predict self-driving attention by
uncertainty modeling and driving knowledge integration. Our approach's
Uncertainty Mining Branch (UMB) discovers commonalities and differences from
multiple generated pseudo-labels achieved from models pre-trained on natural
scenes by actively measuring the uncertainty. Meanwhile, our Knowledge
Embedding Block (KEB) bridges the domain gap by incorporating driving knowledge
to adaptively refine the generated pseudo-labels. Quantitative and qualitative
results with equivalent or even more impressive performance compared to
fully-supervised state-of-the-art approaches across all three public datasets
demonstrate the effectiveness of the proposed method and the potential of this
direction. The code will be made publicly available.
