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TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes
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Proposes TopoLogic, an interpretable lane topology reasoning method that fuses a learnable geometric-distance-to-connectivity mapping with lane-query similarity, improving TOP_ll on OpenLane-V2 and boosting well-trained models via post-processing.
Problem
Lane topology reasoning in autonomous driving relies on vanilla MLPs over lane queries, which are sensitive to endpoint shifts in lane detection, leading to missed connections between lanes.
Approach
Computes connectivity from the endpoint-to-startpoint geometric distance between lanes via a learnable Gaussian-like mapping (scaled by distance std), and from the inner-product similarity of two MLP-encoded lane queries via sigmoid; the two topology matrices are fused with learnable weights and fed to a GNN to refine lane features.
Key results
- On OpenLane-V2 subset_A (v2.1.0, centerline), TopoLogic reaches TOP_ll 23.9 and OLS 44.1 vs. TopoNet's 10.9 and 39.8.
- Applying the geometric-distance post-processing to a well-trained TopoNet raises TOP_ll from 10.9 to 22.3 without retraining.
- On lane segment, TopoLogic achieves TOP_lsls 30.8 (v2.1.0) vs. LaneSegNet's 25.4 and mAP 33.2 vs. 32.6.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TopoLogic instead of TopoNet | Centerline topology reasoning · OpenLane-V2 subset_A, centerline v1.0.0 | TOP_ll: 4.1 -> 18.6 | Helped |
| Use TopoLogic instead of TopoNet | Centerline topology reasoning · OpenLane-V2 subset_A, centerline v2.1.0 | TOP_ll: 10.9 -> 23.9 | Helped |
| Use TopoLogic instead of SMERF | Centerline topology reasoning with SDMap · OpenLane-V2 subset_A, centerline v2.1.0 | TOP_ll: 15.4 -> 28.9 | Helped |
| Use TopoLogic instead of TopoNet | Centerline topology reasoning · OpenLane-V2 subset_B, centerline v1.0.0 | TOP_ll: 2.5 -> 15.1 | Helped |
| Use TopoLogic instead of TopoNet | Centerline topology reasoning · OpenLane-V2 subset_B, centerline v2.1.0 | TOP_ll: 6.7 -> 21.6 | Helped |
| Use TopoLogic instead of LaneSegNet | Lane-segment topology reasoning · OpenLane-V2 subset_A, lane segment v2.0.0 | TOP_lsls: 8.1 -> 22.0 | Helped |
| Use TopoLogic instead of LaneSegNet | Lane-segment topology reasoning · OpenLane-V2 subset_A, lane segment v2.1.0 | TOP_lsls: 25.4 -> 30.8 | Helped |
| Use the learnable geometric mapping function instead of f_tan (ablation) | TopoLogic · OpenLane-V2 subset_A | TOP_ll: 15.1 -> 23.9 | Helped |
| Use the learnable geometric mapping function instead of f_sig (ablation) | TopoLogic · OpenLane-V2 subset_A | TOP_ll: 19.1 -> 23.9 | Helped |
| Use the learnable geometric mapping function instead of f_gau (ablation) | TopoLogic · OpenLane-V2 subset_A | TOP_ll: 21.7 -> 23.9 | Helped |
| Combine lane-query similarity and geometric-distance topology rather than use MLP, similarity-only, or GeoDist-only (ablation) | TopoLogic · OpenLane-V2 subset_A | TOP_ll: 10.8 v.s. 20.1 v.s. 12.9 v.s. 23.9 | Helped |
| Add geometric-distance post-processing (ablation) | TopoNet · OpenLane-V2 subset_A | TOP_ll: 10.9 -> 22.3 | Helped |
Surprising findings
- Applying the geometric distance mapping as post-processing to a trained TopoNet more than doubles its TOP_ll without any retraining.
- The method substantially improves topology but does not substantially elevate lane detection performance, as the authors note.
Limitations
- GNN only aggregates neighbor features, so lane detection gains are limited.
- Geometric distance topology heavily depends on the accuracy of lane detection.
- Research-only; not validated for safety-critical deployment.
Experimental setup
Datasets: OpenLane-V2 ImageNet Argoverse 2 nuScenes
Benchmarks: OpenLane-V2 subset_A OpenLane-V2 subset_B
Metrics: TOP_ll TOP_lt OLS DET_l DET_t TOP_lsls mAP
Baselines: TopoNet SMERF LaneSegNet STSU VectorMapNet MapTR MapTRv2
Builds on: TopoNet Deformable DETR BEVFormer SMERF LaneSegNet
Closest papers by meaning
- TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving (NeurIPS 2025 · similarity 0.95)
- TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning (ICLR 2024 · similarity 0.93)
- Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning (AAAI 2025 · similarity 0.92)
- TopoCurve: Geometry-Aware Topology Reasoning via Bézier Curves in Autonomous Driving (NeurIPS 2026 · similarity 0.91)
- T2SG: Traffic Topology Scene Graph for Topology Reasoning in Autonomous Driving (CVPR 2025 · similarity 0.91)
- LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving (ICLR 2024 · similarity 0.90)
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