  We introduce 2D blind spot estimation as a critical visual task for road
scene understanding. By automatically detecting road regions that are occluded
from the vehicle's vantage point, we can proactively alert a manual driver or a
self-driving system to potential causes of accidents (e.g., draw attention to a
road region from which a child may spring out). Detecting blind spots in full
3D would be challenging, as 3D reasoning on the fly even if the car is equipped
with LiDAR would be prohibitively expensive and error prone. We instead propose
to learn to estimate blind spots in 2D, just from a monocular camera. We
achieve this in two steps. We first introduce an automatic method for
generating ``ground-truth'' blind spot training data for arbitrary driving
videos by leveraging monocular depth estimation, semantic segmentation, and
SLAM. The key idea is to reason in 3D but from 2D images by defining blind
spots as those road regions that are currently invisible but become visible in
the near future. We construct a large-scale dataset with this automatic offline
blind spot estimation, which we refer to as Road Blind Spot (RBS) dataset.
Next, we introduce BlindSpotNet (BSN), a simple network that fully leverages
this dataset for fully automatic estimation of frame-wise blind spot
probability maps for arbitrary driving videos. Extensive experimental results
demonstrate the validity of our RBS Dataset and the effectiveness of our BSN.
