  This report introduces the first-place winning solution for the Autonomous
Grand Challenge 2024 - Mapless Driving. In this report, we introduce a novel
online mapping pipeline LGmap, which adept at long-range temporal model.
Firstly, we propose symmetric view transformation(SVT), a hybrid view
transformation module. Our approach overcomes the limitations of forward sparse
feature representation and utilizing depth perception and SD prior information.
Secondly, we propose hierarchical temporal fusion(HTF) module. It employs
temporal information from local to global, which empowers the construction of
long-range HD map with high stability. Lastly, we propose a novel ped-crossing
resampling. The simplified ped crossing representation accelerates the instance
attention based decoder convergence performance. Our method achieves 0.66
UniScore in the Mapless Driving OpenLaneV2 test set.
