  Lane topology, which is usually modeled by a centerline graph, is essential
for high-level autonomous driving. For a high-quality graph, both topology
connectivity and spatial continuity of centerline segments are critical.
However, most of existing approaches pay more attention to connectivity while
neglect the continuity. Such kind of centerline graph usually cause problem to
planning of autonomous driving. To overcome this problem, we present an
end-to-end network, CGNet, with three key modules: 1)Junction Aware Query
Enhancement module, which provides positional prior to accurately predict
junction points; 2)B\'ezier Space Connection module, which enforces continuity
constraints on any two topologically connected segments in a B\'ezier space; 3)
Iterative Topology Refinement module, which is a graph-based network with
memory to iteratively refine the predicted topological connectivity. CGNet
achieves state-of-the-art performance on both nuScenes and Argoverse2 datasets.
