SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
This addresses the challenge of accurate lane topology modeling for autonomous driving systems, representing an incremental improvement over existing methods.
The paper tackles the problem of modeling complex lane topology for autonomous driving by proposing SeqGrowGraph, a framework that learns lane topology as a chain of graph expansions, achieving state-of-the-art performance on nuScenes and Argoverse 2 datasets.
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 Bézier 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.