  Creating high definition maps that contain precise information of static
elements of the scene is of utmost importance for enabling self driving cars to
drive safely. In this paper, we tackle the problem of drivable road boundary
extraction from LiDAR and camera imagery. Towards this goal, we design a
structured model where a fully convolutional network obtains deep features
encoding the location and direction of road boundaries and then, a
convolutional recurrent network outputs a polyline representation for each one
of them. Importantly, our method is fully automatic and does not require a user
in the loop. We showcase the effectiveness of our method on a large North
American city where we obtain perfect topology of road boundaries 99.3% of the
time at a high precision and recall.
