  High Definition (HD) maps are maps with precise definitions of road lanes
with rich semantics of the traffic rules. They are critical for several key
stages in an autonomous driving system, including motion forecasting and
planning. However, there are only a small amount of real-world road topologies
and geometries, which significantly limits our ability to test out the
self-driving stack to generalize onto new unseen scenarios. To address this
issue, we introduce a new challenging task to generate HD maps. In this work,
we explore several autoregressive models using different data representations,
including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a
hierarchical graph generation model capable of producing high-quality and
diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse
dataset and an in-house dataset show that HDMapGen significantly outperforms
baseline methods. Additionally, we demonstrate that HDMapGen achieves high
scalability and efficiency.
