SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions

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 nn to (n+1)(n+1) to encode connectivity, while a geometric matrix (M) captures centerline shapes as quadratic Bezier 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.