  Online lane graph construction is a promising but challenging task in
autonomous driving. Previous methods usually model the lane graph at the pixel
or piece level, and recover the lane graph by pixel-wise or piece-wise
connection, which breaks down the continuity of the lane. Human drivers focus
on and drive along the continuous and complete paths instead of considering
lane pieces. Autonomous vehicles also require path-specific guidance from lane
graph for trajectory planning. We argue that the path, which indicates the
traffic flow, is the primitive of the lane graph. Motivated by this, we propose
to model the lane graph in a novel path-wise manner, which well preserves the
continuity of the lane and encodes traffic information for planning. We present
a path-based online lane graph construction method, termed LaneGAP, which
end-to-end learns the path and recovers the lane graph via a Path2Graph
algorithm. We qualitatively and quantitatively demonstrate the superiority of
LaneGAP over conventional pixel-based and piece-based methods on challenging
nuScenes and Argoverse2 datasets. Abundant visualizations show LaneGAP can cope
with diverse traffic conditions. Code and models will be released at
\url{https://github.com/hustvl/LaneGAP} for facilitating future research.
