Continuity Preserving Online CenterLine Graph Learning

TL;DR

This paper introduces CGNet, an end-to-end network that enhances lane topology graphs for autonomous driving by preserving spatial continuity and connectivity, leading to improved planning accuracy.

Contribution

The paper proposes a novel CGNet architecture with three modules that jointly improve the continuity and topology of centerline graphs for autonomous driving.

Findings

Achieves state-of-the-art results on nuScenes dataset.

Outperforms existing methods in lane topology prediction.

Enhances planning robustness by preserving spatial continuity.

Abstract

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.