  One of the fundamental challenges to scale self-driving is being able to
create accurate high definition maps (HD maps) with low cost. Current attempts
to automate this process typically focus on simple scenarios, estimate
independent maps per frame or do not have the level of precision required by
modern self driving vehicles. In contrast, in this paper we focus on drawing
the lane boundaries of complex highways with many lanes that contain topology
changes due to forks and merges. Towards this goal, we formulate the problem as
inference in a directed acyclic graphical model (DAG), where the nodes of the
graph encode geometric and topological properties of the local regions of the
lane boundaries. Since we do not know a priori the topology of the lanes, we
also infer the DAG topology (i.e., nodes and edges) for each region. We
demonstrate the effectiveness of our approach on two major North American
Highways in two different states and show high precision and recall as well as
89% correct topology.
