  Today's autonomous vehicles rely extensively on high-definition 3D maps to
navigate the environment. While this approach works well when these maps are
completely up-to-date, safe autonomous vehicles must be able to corroborate the
map's information via a real time sensor-based system. Our goal in this work is
to develop a model for road layout inference given imagery from on-board
cameras, without any reliance on high-definition maps. However, no sufficient
dataset for training such a model exists. Here, we leverage the availability of
standard navigation maps and corresponding street view images to construct an
automatically labeled, large-scale dataset for this complex scene understanding
problem. By matching road vectors and metadata from navigation maps with Google
Street View images, we can assign ground truth road layout attributes (e.g.,
distance to an intersection, one-way vs. two-way street) to the images. We then
train deep convolutional networks to predict these road layout attributes given
a single monocular RGB image. Experimental evaluation demonstrates that our
model learns to correctly infer the road attributes using only panoramas
captured by car-mounted cameras as input. Additionally, our results indicate
that this method may be suitable to the novel application of recommending
safety improvements to infrastructure (e.g., suggesting an alternative speed
limit for a street).
