  High-Definition (HD) maps are essential for the safety of autonomous driving
systems. While existing techniques employ camera images and onboard sensors to
generate vectorized high-precision maps, they are constrained by their reliance
on single-frame input. This approach limits their stability and performance in
complex scenarios such as occlusions, largely due to the absence of temporal
information. Moreover, their performance diminishes when applied to broader
perception ranges. In this paper, we present StreamMapNet, a novel online
mapping pipeline adept at long-sequence temporal modeling of videos.
StreamMapNet employs multi-point attention and temporal information which
empowers the construction of large-range local HD maps with high stability and
further addresses the limitations of existing methods. Furthermore, we
critically examine widely used online HD Map construction benchmark and
datasets, Argoverse2 and nuScenes, revealing significant bias in the existing
evaluation protocols. We propose to resplit the benchmarks according to
geographical spans, promoting fair and precise evaluations. Experimental
results validate that StreamMapNet significantly outperforms existing methods
across all settings while maintaining an online inference speed of $14.2$ FPS.
Our code is available at https://github.com/yuantianyuan01/StreamMapNet.
