Papers › Graph-based Topology Reasoning for Driving Scenes

Graph-based Topology Reasoning for Driving Scenes

11 Apr 2023arXiv:2304.05277archive 2025-07-28

Tianyu Li, Li Chen, Huijie Wang, Yang Li, Jiazhi Yang, Xiangwei Geng, Shengyin Jiang, Yuting Wang, Hang Xu, Chunjing Xu, Junchi Yan, Ping Luo, Hongyang Li

Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code is released at https://github.com/OpenDriveLab/TopoNet

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denormalize_3dlane opendrivelab/toponet/projects/toponet/core/lane/util.py official repository unverified Apache-2.0 (permissive) · 12011d62cff184fa · report
draw_corner_rectangle opendrivelab/toponet/projects/toponet/core/visualizer/lane.py official repository unverified Apache-2.0 (permissive) · 3ca2707d65a7dc11 · report
normalize_3dlane opendrivelab/toponet/projects/toponet/core/lane/util.py official repository unverified Apache-2.0 (permissive) · 2d44c73be8e6fb9d · report
show_bev_results opendrivelab/toponet/projects/toponet/core/visualizer/lane.py official repository unverified Apache-2.0 (permissive) · 7fd467b52ada2cd2 · report

Tasks

3D Lane DetectionAutonomous DrivingGraph Neural NetworkScene Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Lane Detection OpenLane-V2 val TopoNet DET_l 28.5 #7 of 9 Archive leaderboard report
3D Lane Detection OpenLane-V2 val TopoNet DET_t 48.1 #7 of 9 Archive leaderboard report
3D Lane Detection OpenLane-V2 val TopoNet OLS 35.6 #7 of 9 Archive leaderboard report
3D Lane Detection OpenLane-V2 val TopoNet TOP_ll 4.1 #7 of 9 Archive leaderboard report
3D Lane Detection OpenLane-V2 val TopoNet TOP_lt 20.8 #7 of 9 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Graph Neural Network

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