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One of the fundamental challenges to scale self-driving is being able tocreate accurate high definition maps (HD maps) with low cost. Current attemptsto automate this process typically focus on simple scenarios, estimateindependent maps per frame or do not have the level of precision required bymodern self driving vehicles. In contrast, in this paper we focus on drawingthe lane boundaries of complex highways with many lanes that contain topologychanges due to forks and merges. Towards this goal, we formulate the problem asinference in a directed acyclic graphical model (DAG), where the nodes of thegraph encode geometric and topological properties of the local regions of thelane boundaries. Since we do not know a priori the topology of the lanes, wealso infer the DAG topology (i.e., nodes and edges) for each region. Wedemonstrate the effectiveness of our approach on two major North AmericanHighways in two different states and show high precision and recall as well as89% correct topology.

DAGMapper: Learning to Map by Discovering Lane Topology