  Autonomous driving systems require High-Definition (HD) semantic maps to
navigate around urban roads. Existing solutions approach the semantic mapping
problem by offline manual annotation, which suffers from serious scalability
issues. Recent learning-based methods produce dense rasterized segmentation
predictions to construct maps. However, these predictions do not include
instance information of individual map elements and require heuristic
post-processing to obtain vectorized maps. To tackle these challenges, we
introduce an end-to-end vectorized HD map learning pipeline, termed
VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a
sparse set of polylines in the bird's-eye view. This pipeline can explicitly
model the spatial relation between map elements and generate vectorized maps
that are friendly to downstream autonomous driving tasks. Extensive experiments
show that VectorMapNet achieve strong map learning performance on both nuScenes
and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2
mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating
comprehensive maps and capturing fine-grained details of road geometry. To the
best of our knowledge, VectorMapNet is the first work designed towards
end-to-end vectorized map learning from onboard observations. Our project
website is available at
\url{https://tsinghua-mars-lab.github.io/vectormapnet/}.
