  High-definition (HD) map provides abundant and precise static environmental
information of the driving scene, serving as a fundamental and indispensable
component for planning in autonomous driving system. In this paper, we present
\textbf{Map} \textbf{TR}ansformer, an end-to-end framework for online
vectorized HD map construction. We propose a unified permutation-equivalent
modeling approach, \ie, modeling map element as a point set with a group of
equivalent permutations, which accurately describes the shape of map element
and stabilizes the learning process. We design a hierarchical query embedding
scheme to flexibly encode structured map information and perform hierarchical
bipartite matching for map element learning. To speed up convergence, we
further introduce auxiliary one-to-many matching and dense supervision. The
proposed method well copes with various map elements with arbitrary shapes. It
runs at real-time inference speed and achieves state-of-the-art performance on
both nuScenes and Argoverse2 datasets. Abundant qualitative results show stable
and robust map construction quality in complex and various driving scenes. Code
and more demos are available at \url{https://github.com/hustvl/MapTR} for
facilitating further studies and applications.
