  High-definition (HD) map provides abundant and precise environmental
information of the driving scene, serving as a fundamental and indispensable
component for planning in autonomous driving system. We present MapTR, a
structured end-to-end Transformer for efficient online vectorized HD map
construction. We propose a unified permutation-equivalent modeling approach,
i.e., 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. MapTR achieves the best
performance and efficiency with only camera input among existing vectorized map
construction approaches on nuScenes dataset. In particular, MapTR-nano runs at
real-time inference speed ($25.1$ FPS) on RTX 3090, $8\times$ faster than the
existing state-of-the-art camera-based method while achieving $5.0$ higher mAP.
Even compared with the existing state-of-the-art multi-modality method,
MapTR-nano achieves $0.7$ higher mAP, and MapTR-tiny achieves $13.5$ higher mAP
and $3\times$ faster inference speed. Abundant qualitative results show that
MapTR maintains stable and robust map construction quality in complex and
various driving scenes. MapTR is of great application value in autonomous
driving. Code and more demos are available at
\url{https://github.com/hustvl/MapTR}.
