SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions

• URL: http://arxiv.org/abs/2507.04822v1
• Date: Mon, 07 Jul 2025 09:42:37 GMT
• Title: SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
• Authors: Mengwei Xie, Shuang Zeng, Xinyuan Chang, Xinran Liu, Zheng Pan, Mu Xu, Xing Wei,
• Abstract summary: We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions.<n>We evaluate it on nuScenes and Argoverse 2 datasets.
• Score: 17.302926840794193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures-such as loops and bidirectional lanes-prevalent in real-world road structure. We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions, inspired by human map-drawing processes. Representing the lane graph as a directed graph $G=(V,E)$, with intersections ($V$) and centerlines ($E$), SeqGrowGraph incrementally constructs this graph by introducing one vertex at a time. At each step, an adjacency matrix ($A$) expands from $n \times n$ to $(n+1) \times (n+1)$ to encode connectivity, while a geometric matrix ($M$) captures centerline shapes as quadratic B\'ezier curves. The graph is serialized into sequences, enabling a transformer model to autoregressively predict the chain of expansions, guided by a depth-first search ordering. Evaluated on nuScenes and Argoverse 2 datasets, SeqGrowGraph achieves state-of-the-art performance.

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