  Vectorized high-definition (HD) maps contain detailed information about
surrounding road elements, which are crucial for various downstream tasks in
modern autonomous vehicles, such as motion planning and vehicle control. Recent
works attempt to directly detect the vectorized HD map as a point set
prediction task, achieving notable detection performance improvements. However,
these methods usually overlook and fail to analyze the important inner-instance
correlations between predicted points, impeding further advancements. To
address this issue, we investigate the utilization of inner-instance
information for vectorized high-definition mapping through transformers, and
propose a powerful system named $\textbf{InsMapper}$, which effectively
harnesses inner-instance information with three exquisite designs, including
hybrid query generation, inner-instance query fusion, and inner-instance
feature aggregation. The first two modules can better initialize queries for
line detection, while the last one refines predicted line instances. InsMapper
is highly adaptable and can be seamlessly modified to align with the most
recent HD map detection frameworks. Extensive experimental evaluations are
conducted on the challenging NuScenes and Argoverse 2 datasets, where InsMapper
surpasses the previous state-of-the-art method, demonstrating its effectiveness
and generality. The project page for this work is available at
https://tonyxuqaq.github.io/InsMapper/ .
