Leveraging Enhanced Queries of Point Sets for Vectorized Map
  Construction
Zihao Liu Xiaoyu Zhang Guangwei Liu Ji Zhao Ningyi Xu
In autonomous driving, the high-definition (HD) map plays a crucial role in
localization and planning. Recently, several methods have facilitated
end-to-end online map construction in DETR-like frameworks. However, little
attention has been paid to the potential capabilities of exploring the query
mechanism. This paper introduces MapQR, an end-to-end method with an emphasis
on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR
utilizes instance queries rather than point queries. These instance queries are
scattered for the prediction of point sets and subsequently gathered for the
final matching. This query design, called the scatter-and-gather query, shares
content information in the same map element and avoids possible inconsistency
of content information in point queries. We further exploit prior information
to enhance an instance query by adding positional information embedded from
their reference points. Together with a simple and effective improvement of a
BEV encoder, the proposed MapQR achieves the best mean average precision (mAP)
and maintains good efficiency on both nuScenes and Argoverse 2. In addition,
integrating our query design into other models can boost their performance
significantly. The code will be available at https://github.com/HXMap/MapQR.