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.

本文通过使用有向无环图模型 (DAG) 来推断车道边界的几何和拓扑属性，从而提高制作高精度地图的效率。经测试，该方法在两个不同的州的两条北美高速公路上表现出了 89% 的正确率。

DAGMapper: 发现车道拓扑结构的映射学习

DAGMapper: Learning to Map by Discovering Lane Topology

We describe computationally efficient methods for learning mixtures in which
each component is a directed acyclic graphical model (mixtures of DAGs or
MDAGs). We argue that simple search-and-score algorithms are infeasible for a
variety of problems, and introduce a feasible approach in which parameter and
structure search is interleaved and expected data is treated as real data. Our
approach can be viewed as a combination of (1) the Cheeseman--Stutz asymptotic
approximation for model posterior probability and (2) the
Expectation--Maximization algorithm. We evaluate our procedure for selecting
among MDAGs on synthetic and real examples.

本文通过 Cheeseman-Stutz 渐近逼近和 Expectation-Maximization 算法的结合，提出了一种有效的方法来学习混合有向无环图模型（MDAGs），并探讨了参数和模型结构的搜索问题。