  Vectorized High-Definition (HD) map construction requires predictions of the
category and point coordinates of map elements (e.g. road boundary, lane
divider, pedestrian crossing, etc.). State-of-the-art methods are mainly based
on point-level representation learning for regressing accurate point
coordinates. However, this pipeline has limitations in obtaining element-level
information and handling element-level failures, e.g. erroneous element shape
or entanglement between elements. To tackle the above issues, we propose a
simple yet effective HybrId framework named HIMap to sufficiently learn and
interact both point-level and element-level information. Concretely, we
introduce a hybrid representation called HIQuery to represent all map elements,
and propose a point-element interactor to interactively extract and encode the
hybrid information of elements, e.g. point position and element shape, into the
HIQuery. Additionally, we present a point-element consistency constraint to
enhance the consistency between the point-level and element-level information.
Finally, the output point-element integrated HIQuery can be directly converted
into map elements' class, point coordinates, and mask. We conduct extensive
experiments and consistently outperform previous methods on both nuScenes and
Argoverse2 datasets. Notably, our method achieves $77.8$ mAP on the nuScenes
dataset, remarkably superior to previous SOTAs by $8.3$ mAP at least.
