  Inferring traffic object such as lane information is of foremost importance
for deployment of autonomous driving. Previous approaches focus on offline
construction of HD map inferred with GPS localization, which is insufficient
for globally scalable autonomous driving. To alleviate these issues, we propose
online HD map learning framework that detects HD map elements from onboard
sensor observations. We represent the map elements as a graph; we propose
InstaGraM, instance-level graph modeling of HD map that brings accurate and
fast end-to-end vectorized HD map learning. Along with the graph modeling
strategy, we propose end-to-end neural network composed of three stages: a
unified BEV feature extraction, map graph component detection, and association
via graph neural networks. Comprehensive experiments on public open dataset
show that our proposed network outperforms previous models by up to 13.7 mAP
with up to 33.8X faster computation time.
