  Understanding road structures is crucial for autonomous driving. Intricate
road structures are often depicted using lane graphs, which include centerline
curves and connections forming a Directed Acyclic Graph (DAG). Accurate
extraction of lane graphs relies on precisely estimating vertex and edge
information within the DAG. Recent research highlights Transformer-based
language models' impressive sequence prediction abilities, making them
effective for learning graph representations when graph data are encoded as
sequences. However, existing studies focus mainly on modeling vertices
explicitly, leaving edge information simply embedded in the network.
Consequently, these approaches fall short in the task of lane graph extraction.
To address this, we introduce LaneGraph2Seq, a novel approach for lane graph
extraction. It leverages a language model with vertex-edge encoding and
connectivity enhancement. Our serialization strategy includes a vertex-centric
depth-first traversal and a concise edge-based partition sequence.
Additionally, we use classifier-free guidance combined with nucleus sampling to
improve lane connectivity. We validate our method on prominent datasets,
nuScenes and Argoverse 2, showcasing consistent and compelling results. Our
LaneGraph2Seq approach demonstrates superior performance compared to
state-of-the-art techniques in lane graph extraction.
