mlsanityData to 7 Oct 2026

Continuity Preserving Online CenterLine Graph Learning

arXiv 2407.11337 ↗First posted 16 Jul 2024cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 52 of its 59 references that are in the map. First posted on arXiv 16 Jul 2024.

Builds on

52 of 59 references.

References · 52

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38 more
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50 most cited of 52 All 52 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.

  1. 2024TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problemcited by 7
  2. 2024TopoBDA: Towards Bezier Deformable Attention for Road Topology Understandingcited by 5
  3. 2025LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generationcited by 5
  4. 2026Unified Modeling of Lane and Lane Topology for Driving Scene Reasoningcited by 1
  5. 2026Generative Lane Topology Reasoning via Autoregressive Model with Geometry Priorcited by 0