T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving

Changsheng Lv, Mengshi Qi, Liang Liu, Huadong Ma; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 17197-17206

Understanding the traffic scenes and then generating high-definition (HD) maps present significant challenges in autonomous driving. In this paper, we defined a novel \underline T raffic \underline T opology \underline S cene \underline G raph (\text T ^2\text SG ), a unified scene graph explicitly modeling the lane, controlled and guided by different road signals ( e.g. , right turn), and topology relationships among them, which is always ignored by previous high-definition (HD) mapping methods. For the generation of \text T ^2\text SG , we propose TopoFormer, a novel one-stage \underline Topo logy Scene Graph Trans\underline Former with two newly-designed layers. Specifically, TopoFormer incorporates a Lane Aggregation Layer (LAL) that leverages the geometric distance among the centerline of lanes to guide the aggregation of global information. Furthermore, we proposed a Counterfactual Intervention Layer (CIL) to model the reasonable road structure ( e.g. , intersection, straight) among lanes under counterfactual intervention. Then the generated \text T ^2\text SG can provide a more accurate and explainable description of the topological structure in traffic scenes. Experimental results demonstrate that TopoFormer outperforms existing methods on the \text T ^2\text SG generation task, and the generated \text T ^2\text SG significantly enhances traffic topology reasoning in downstream tasks, achieving a state-of-the-art performance of 46.3 OLS on the OpenLane-V2 benchmark. Our source code is available at https://github.com/MICLAB-BUPT/T2SG.