  The majority of current approaches in autonomous driving rely on
High-Definition (HD) maps which detail the road geometry and surrounding area.
Yet, this reliance is one of the obstacles to mass deployment of autonomous
vehicles due to poor scalability of such prior maps. In this paper, we tackle
the problem of online road map extraction via leveraging the sensory system
aboard the vehicle itself. To this end, we design a structured model where a
graph representation of the road network is generated in a hierarchical fashion
within a fully convolutional network. The method is able to handle complex road
topology and does not require a user in the loop.
