  High-definition (HD) map is crucial for autonomous driving systems. Most
existing works design map elements detection heads based on the DETR decoder.
However, the initial queries lack explicit incorporation of physical positional
information, and vanilla self-attention entails high computational complexity.
Therefore, we propose EAN-MapNet for Efficiently constructing HD map using
Anchor Neighborhoods. Firstly, we design query units based on the anchor
neighborhoods, allowing non-neighborhood central anchors to effectively assist
in fitting the neighborhood central anchors to the target points representing
map elements. Then, we propose grouped local self-attention (GL-SA) by
leveraging the relative instance relationship among the queries. This
facilitates direct feature interaction among queries of the same instances,
while innovatively employing local queries as intermediaries for interaction
among queries from different instances. Consequently, GL-SA significantly
reduces the computational complexity of self-attention while ensuring ample
feature interaction among queries. On the nuScenes dataset, EAN-MapNet achieves
a state-of-the-art performance with 63.0 mAP after training for 24 epochs,
surpassing MapTR by 12.7 mAP. Furthermore, it considerably reduces memory
consumption by 8198M compared to MapTRv2.
