Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior

Lane topology reasoning aims to construct a lane graph fromonboard sensor observations. Existing methods follow a detection and as-sociation paradigm that treats each lane instance independently, leadingto geometric inconsistency at connected endpoints and incomplete graphsdue to visual occlusions. To address these issues, we propose TopoGPT,a generative framework that learns the geometry prior from typical lanegraph structures through autoregressive sequence modeling. Specifically,we construct a large-scale map dataset comprising 3.3M scenes. For eachlane graph, a lane tokenizer serializes it into discrete tokens, while a scenecontext encoder converts it into a rasterized image and extracts globalfeatures as scene tokens. We pre-train an autoregressive lane sequencetransformer via scene-conditioned next-token prediction, endowing themodel with the geometry prior over lane graph structures. Building uponthis prior, a perception adapter aligns BEV features from multi-view im-ages with the pre-trained scene condition, transferring the learned geom-etry prior to sensor-based lane graph prediction. On the OpenLane-V2benchmark, TopoGPT outperforms existing methods by an average of+6.4 on lane-level and +11.6 on point-level metrics, and produces geo-metrically consistent and structurally complete lane graphs. Our projectpage is available at https://buaa-colalab.github.io/topogpt_page.