Continuity Preserving Online CenterLine Graph Learning
This work addresses the critical need for accurate lane topology modeling in autonomous driving planning, representing an incremental improvement by focusing on continuity constraints.
The paper tackles the problem of generating high-quality lane centerline graphs for autonomous driving by addressing both topology connectivity and spatial continuity, which existing methods often neglect, and presents CGNet, an end-to-end network that achieves state-of-the-art performance on nuScenes and Argoverse2 datasets.
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)Bézier Space Connection module, which enforces continuity constraints on any two topologically connected segments in a Bézier 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.