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Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology

Xiaoqi Wang, Dingyi Zhaung, David Paz, Wenbin He, Yucai Bai, Peng Zhou, Rui Zhang, Jinhua Zhao, Liu Ren

arXiv:2610.11058v1cs.CV

TL;DR

Existing driving-topology methods leave a gap to the detection-conditioned connectivity upper bound, and thresholded scores can yield unreliable decision-ready graphs. TopoEnhance learns to reconstruct structural consistency from corrupted topology graphs. Across diverse sources, it improves continuous TOP scores and discrete TJS without retraining.

  • Problem

    Existing methods leave a gap to the connectivity Oracle given their detections, while thresholded scores can produce unreliable discrete graphs that benchmarks rarely evaluate directly.

    Source: §1 · p. 2

  • Method

    TopoEnhance learns to refine topology connections by reconstructing structural consistency from stochastically corrupted ground-truth graphs.

    Source: §1 · p. 3, §3.2 · p. 5

  • Results

    TopoEnhance consistently improves TOP ranking and discrete TJS across diverse sources without retraining.

    Source: §1 · pp. 2–3, §5 · p. 10

  • Takeaways & Limitations

    The framework produces more reliable decision-ready topology graphs by resolving logical inconsistencies and recovering structural consistency.

    Source: §5 · p. 10

    Lane–traffic refinement requires camera images; without them, TopoEnhance can still refine lane–lane connectivity.

    Source: F LIMITATION AND FUTURE WORKS · pp. 24–25

Abstract

from arXiv · show

In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motion control. While current methods excel at detecting individual map elements, their connectivity reasoning often falls short of its theoretical potential, leaving a significant performance gap relative to the theoretical upper-bound achievable given the underlying detections. Furthermore, the decision-ready topology graphs passed to downstream tasks often remain unreliable. Current approaches typically derive connectivity by thresholding continuous topology scores; however, these scores often fail to reflect the true logical likelihood of connectivity, resulting in false positives or missing connections. Existing benchmarks further overlook this issue by primarily evaluating continuous metrics, rather than assessing the discrete connectivity required for decision-making. To bridge these gaps, we propose TopoEnhance, a novel topology enhancement framework designed to unlock the latent potential of existing methods and improve the reliability of decision-ready topology. We formulate topology enhancement as a denoising-based reconstruction process, where the model learns to recover structural consistency from stochastically corrupted ground-truth graphs. This formulation enables the model to resolve logical inconsistencies and rectify unreliable connections, producing robust discrete topology graphs that closely approach theoretical maximum performance. Extensive experiments across different baselines show that TopoEnhance consistently improves both continuous topology metrics (TOP score), and discrete connectivity measured by our adapted Topology Jaccard Similarity (TJS) metric. As a flexible, source-agnostic framework, TopoEnhance delivers substantial gains across diverse state-of-the-art baselines without requiring retraining.

1 INTRODUCTION

Driving topology connects lanes into paths and links traffic elements to the lanes they govern. TopoEnhance addresses gaps in connectivity reasoning and decision-ready graph reliability, improving continuous and discrete topology metrics across diverse sources without retraining.

1 INTRODUCTION

  • A driving topology graph represents lanes and traffic elements as nodes and their relations as edges, supporting lane–lane paths and lane–traffic associations.These relations help determine which lane to follow and which traffic signals govern movement during a turn.Source: §1 · pp. 1–2
  • Even with perfect map-element detections, existing methods leave a gap to the connectivity-reasoning Oracle, indicating unused structural information in their predictions.Incorrect connections can still lead a planning module to choose an unsafe route.Source: §1 · p. 2
  • Thresholded topology scores can misrepresent connection likelihoods, producing false positives or missing links, while benchmarks emphasize continuous TOP scores over discrete graph correctness.The authors identify reliable decision-ready topology as a practical need for downstream path planning.Source: §1 · p. 2
  • TopoEnhance frames refinement as denoising: it corrupts ground-truth topology graphs stochastically and learns to recover reliable, structurally consistent connections.The framework is intended to exploit structural information in existing detections.Source: §1 · pp. 2–3
  • TopoEnhance consistently improves TOPll, TOPlt, and TJS across diverse sources without retraining.For example, it raises TopoLogic TOPlt from 25.4 to 27.2 and SMART TOPll from 37.0 to 40.1.Source: §1 · pp. 2–3

2 RELATED WORK

Prior driving-topology methods predict lane–lane and lane–traffic relations using varied architectures and input cues, but commonly threshold pairwise confidence scores into discrete graphs. TopoEnhance instead refines these connections with heterogeneous structural context.

Driving Topology Reasoning

  • Driving-topology systems range from joint DETR-style prediction and GNN or MLP reasoning to geometric–semantic, point–lane, map, imagery, and lane-prior approaches.The cited examples include STSU, TopoNet, TopoMLP, TopoLogic, TopoPoint, SMERF, TopoSD, SMART, and Topo2D.Source: §2 · p. 3
  • Across these approaches, topology is commonly represented by pairwise confidence scores thresholded into discrete graphs, while discrete connectivity reliability is rarely explicitly optimized or analyzed.The passage notes that scores may fail to reflect logical likelihoods and that existing methods remain below the theoretical upper bound.Source: §2 · p. 3

Graph Neural Networks

  • TopoEnhance uses distinct message-passing kernels for lane–lane and lane–traffic relations, with graph attention weighting neighbors to emphasize structurally consistent connections and suppress noise.The heterogeneous attention mechanism is described as allowing traffic-rule-aware patterns to be learned implicitly.Source: §2 · p. 3

3 TOPOENHANCE

TopoEnhance treats predicted connectivity as a corrupted graph and learns to reconstruct reliable, structurally consistent topology through simulated corruption and heterogeneous graph denoising.

3.1 PROBLEM FORMULATION

  • Thresholded connectivity scores can yield false-positive or false-negative edges, so the framework maps detections and scores to calibrated scores using heterogeneous neighborhood context.The scene graph contains lane and traffic-element nodes, with lane–lane and lane–traffic edges.Source: §3.1 · p. 4

3.2 FORWARD PROCESS: TOPOLOGY NOISE SIMULATION

  • The forward process stochastically corrupts ground-truth graphs to approximate real prediction errors, helping learned corrections transfer to inference predictions without source-specific retraining.The corruption distribution must align with real predictions because otherwise the reverse process may not generalize.Source: §3.2 · p. 5
  • False-positive detections are added to the graph, and candidate edges receive positive labels only when they match ground-truth edges.The augmented node count is matched to the expected inference detection count so the candidate edge space matches the raw topology scores.Source: §3.2 · p. 5
  • A noise curriculum shifts true-positive jitter upward and false-positive displacement downward toward δtype, while keeping jittered detections semantically valid and distinguishable from false positives.This encourages connectivity decisions based on topological context rather than exact coordinate matching.Source: §3.2 · p. 5

3.3 REVERSE PROCESS: TOPOLOGY ENHANCEMENT MODEL

  • A heterogeneous GNN uses type-aware message passing and attention over lane and traffic nodes to suppress inconsistent edges and reinforce structurally aligned connections.The model uses lane polylines and traffic-element visual features as node inputs, then computes refined scores from final node embeddings.Source: §3.3 · p. 6
  • Relation-aware, volume-normalized BCE prevents the much larger lane–lane candidate space from dominating learning for lane–traffic relations.The objective is interpreted as a relation-balanced reconstruction term in a single-step T=1 diffusion ELBO.Source: §3.3 · p. 6
  • At inference, the model refines raw topology scores, fuses them with the original scores using relation-specific weights, and thresholds the result into a discrete graph.The trained enhancer can calibrate topology scores from any reasoning method without retraining.Source: Figure 1 · p. 4, §3.3 · pp. 6–7

4 EXPERIMENTS

Experiments evaluate TopoEnhance on OpenLane-V2 against fixed detections and official baseline checkpoints, measuring both continuous score ranking and discrete graph quality. Enhancement improves both metric families across baselines, with qualitative examples of repaired connections.

4 EXPERIMENTS

  • Experiments use OpenLane-V2 and one model per dataset to enhance predictions at inference without method-specific retraining; evaluation is restricted to methods with official checkpoints.The evaluated checkpoints include TopoNet, TopoMLP, Topo2D, TopoLogic, and SMART.Source: §4 · p. 7, §4.2 · p. 8

4.1 EVALUATION METRICS

  • OpenLane-V2 TOP evaluates continuous connection-score ranking, while TJS measures overlap between thresholded predicted and ground-truth edge sets.TOPll and TOPlt distinguish lane–lane and lane–traffic relations; TJS penalizes both false positives and false negatives.Source: §4.1 · p. 7
  • The theoretical upper bound measures the maximum TOP and TJS achievable with the existing detections, leaving errors from missing or false-positive detections in place.This ceiling separates connectivity-reasoning headroom from errors inherent to the detector.Source: §4.1 · p. 8

4.2 ENHANCED RESULTS

  • For SMART, TOPll rises 37.0→40.1 and TOPlt 33.0→35.4; enhanced TOPll is within 0.8 of its theoretical ceiling.Across evaluated baselines, TopoEnhance improves TOP, and the reported Argoverse2 TOPll margins to the ceiling are 0.6 for TopoMLP and 0.7 for Topo2D.Source: §4.2 · p. 8, Table 1 · p. 9
  • TopoMLP's TJSll rises 18.5→59.7 and TJSlt 31.5→59.8; SMART's TJSll rises 38.1→87.7 and TJSlt 36.6→82.8.The reported comparisons show substantial gains in final decision-ready topology graphs.Source: §4.2 · pp. 8–9, Table 2 · p. 9
  • Qualitative examples show TopoEnhance correcting missing or fragmented links, including unlinked traffic lights in TopoNet and inconsistent lane merges in TopoMLP.Figure 2 compares topology connections before and after enhancement.Source: §4.2 · p. 9, Figure 2 · p. 9

5 CONCLUSION

The conclusion presents TopoEnhance as a source-agnostic framework for improving decision-ready topology, and the appendix interprets its training objective as single-step diffusion denoising. Its forward process creates noisy graphs from ground truth by jittering true-positive detections and synthesizing false positives.

5 CONCLUSION

  • TopoEnhance reconstructs stochastically corrupted topology graphs to resolve logical inconsistencies, improving TOP and TJS across diverse sources without retraining.The conclusion frames structural refinement as a step toward closing the decision-ready topology performance gap.Source: §5 · p. 10

A THEORETICAL DIFFUSION-BASED INTERPRETATION OF TOPOENHANCE

  • Under the stated fixed-prior and conditional-independence assumptions, TopoEnhance's normalized relation-aware BCE is a relation-balanced reconstruction term in a T=1 diffusion ELBO.The appendix identifies the objective as a relation-balanced, volume-normalized Bernoulli negative log-likelihood over candidate edges.Source: A THEORETICAL DIFFUSION-BASED… · pp. 13, 15

B ALGORITHMIC SUMMARY OF FORWARD PROCESS: TOPOLOGY NOISE SIMULATION

  • The forward noise simulation jitters true-positive detections, synthesizes false-positive detections, forms candidate edges, and labels them against the ground-truth graph.The algorithm applies an adaptive noise curriculum and scales the noisy vertex set to match the expected detection count.Source: B ALGORITHMIC SUMMARY OF FORWARD… · p. 15

C IMPLEMENTATION DETAILS

TopoEnhance uses a lightweight refinement model and is evaluated on geographically non-overlapping data and TopoPoint predictions. Across these settings, enhancement improves topology metrics, including under a weaker nuScenes reproduction.

C IMPLEMENTATION DETAILS

  • TopoEnhance takes about 16 hours to train and 0.1 ms per sample at inference, with discrete connections derived using a 0.5 threshold.The implementation uses a two-layer GATv2 encoder with 64 hidden dimensions and eight attention heads.Source: C IMPLEMENTATION DETAILS · p. 15

D ADDITIONAL EVALUATION RESULTS

  • Additional evaluations test geographic overlap, TopoPoint predictions, complete OpenLane-V2 metrics, and feature, loss, and jitter ablations.The main experiments use released checkpoints when sample-level detections and topology confidence matrices are available.Source: D ADDITIONAL EVALUATION RESULTS · p. 16, §D.2 · p. 16

D.1 TOPOENHANCE ON GEOGRAPHICALLY NON-OVERLAPPING SPLITS

  • On non-overlapping GeoSplits, TOPll rises from 3.34 to 10.23 and TJSlt from 20.49 to 35.49 with TopoEnhance.Both TopoNet and TopoEnhance are trained on the geographically non-overlapping splits.Source: §D.1 · p. 16, Table 3 · p. 16

D.2 ENHANCING TOPOPOINT PREDICTIONS

  • On Argoverse2, enhancing TopoPoint raises TOPlt from 30.36 to 32.28, TJSll from 22.31 to 25.03, and TJSlt from 35.94 to 55.13.The evaluation uses TopoPoint checkpoints trained with its official implementation; Table 4 distinguishes reported results from the authors’ reproduction and omits TJS entries not reported by TopoPoint.Source: §D.2 · p. 16, Table 4 · p. 17
  • On nuScenes Subset_B, TopoEnhance improves all four topology metrics in the authors’ reproduction, but the lane detection result remains below the reported result.The reproduction reports DETl = 23.67 versus 31.20, so these results are explicitly not presented as an exact reproduction.Source: §D.2 · pp. 16–17

D.3 COMPLETE OPENLANE-V2 METRICS

TopoEnhance improves overall OpenLane-V2 scores while leaving upstream detection outputs unchanged.

D.3 COMPLETE OPENLANE-V2 METRICS

  • TopoEnhance refines topology connections without changing lane or traffic-element detection metrics, which remain identical before and after enhancement.Table 5 reports DETl, DETt, and OLS alongside TOPll and TOPlt.Source: §D.3 · p. 17
  • Because detection terms remain fixed in the OLS calculation, its gains after enhancement are attributable to improved topology reasoning.OLS combines detection and topology reasoning under the standard OpenLane-V2 protocol.Source: §D.3 · p. 17
  • OLS increases from 36.5 to 41.4 for TopoNet on nuScenes and from 53.1 to 54.3 for SMART on Argoverse2.Enhanced OLS values consistently move toward their detection-conditioned Upper-Bounds.Source: §D.3 · p. 17

D.4 ABLATION STUDIES

Ablations examine traffic-element visual features, loss functions, and geometric localization jitter. The additional practical-usability evaluations cover robustness, temporal consistency, safety-relevant errors, route recoverability, and computational efficiency.

D.4 ABLATION STUDIES

  • DINOv2-ViT-L achieves the highest overall lane–traffic performance, with TOPlt of 25.8; lane–lane TOPll remains relatively stable across most high-capacity backbones.The performance gap between ResNet-50 and DINOv2 ViT Large is 0.2, and increasing to DINOv2-ViT-G brings no further gain.Source: §D.4 · p. 18, Table 6 · p. 18
  • Normalized BCE balances lane–lane and lane–traffic learning by scaling each relation category by its candidate-edge volume.This avoids dense lane–lane candidates overwhelming the sparser lane–traffic associations and reduces reliance on manually tuned weights.Source: §D.4 · pp. 18–19, Table 7 · p. 18
  • Adding geometric localization jitter improves both metrics, including a +0.4 gain in TOPlt.Table 8 isolates jitter while keeping the other topology-noise simulation components unchanged.Source: §D.4 · p. 19, Table 8 · p. 19

E PRACTICAL USABILITY TOWARD REAL-WORLD APPLICATIONS

  • The practical-usability evaluations examine corruption robustness, frame-to-frame consistency, safety-relevant errors, ego-route recoverability, and computational efficiency.These are presented as additional evaluations across Appendices E.1–E.5.Source: E PRACTICAL USABILITY TOWARD… · p. 19

E.1 ROBUSTNESS TO VARYING LEVELS OF TOPOLOGY NOISE

TopoEnhance is tested under controlled topology-score corruption and for consistency across consecutive frames. It improves topology metrics across corruption levels and reduces both edge flips and score variation across four baselines.

E.1 ROBUSTNESS TO VARYING LEVELS OF TOPOLOGY NOISE

  • The controlled corruption experiment independently replaces each predicted topology score s_uv with 1 − s_uv at probabilities from 0.0 to 0.9.It is conducted on Argoverse2 Subset_A for TopoNet and SMART; p = 0 denotes the uncorrupted predictions reported in the main paper.Source: §E.1 · p. 20
  • Small score corruption can sharply reduce TOP, while further corruption has a smaller marginal effect as score rankings become increasingly disrupted.The paper attributes this nonlinear degradation to TOP’s mAP basis and the sensitivity of Average Precision to errors near the top of the ranking.Source: §E.1 · p. 20
  • Even at p = 0.9 corruption, SMART improves from 0.33 to 2.20 on TOPll and from 2.20 to 2.81 on TOPlt with TopoEnhance.Across the full corruption range, TOPll and TOPlt improve for both TopoNet and SMART; the same trained enhancer is used without retraining or adaptation.Source: §E.1 · p. 20, Table 9 · p. 20

E.2 TEMPORAL CONSISTENCY OF REFINED TOPOLOGY PREDICTIONS

  • Across all four baselines, TopoEnhance reduces both discrete edge flip rates and temporal standard deviations of topology scores.The evaluations use lane pairs co-visible for at least five consecutive frames; lower values indicate fewer flips and more stable confidence, respectively.Source: §E.2 · p. 21, Table 10 · p. 21, Table 11 · p. 21

E.3 SAFETY-RELEVANT TOPOLOGY ERROR ANALYSIS

TopoEnhance improves safety-relevant connectivity and full-route recoverability across baselines. The analyses isolate topology quality from detection errors and examine whether local corrections preserve complete ego routes.

E.3 SAFETY-RELEVANT TOPOLOGY ERROR ANALYSIS

  • Across four baselines, TopoEnhance corrects 7–60 missed ego successors and 102–1,502 missed intersection turns, while breaking at most 16 turns for SMART and none for the other methods.Only relations with endpoint detections successfully matched to ground truth are evaluated.Source: §E.3 · p. 22, Table 12 · p. 22
  • On Argoverse2 (Subset_A), across four baselines and evaluated relations with endpoints matched to ground truth, TopoEnhance recovers 102–112 red-light, 279–316 green-light, 22–43 yellow-light, and 25–42 turn-signal associations, without new errors.Source: §E.3 · p. 22, Table 12 · p. 22
  • Recovering ego-route successors and intersection turns preserves feasible-path connectivity, while lane–traffic associations provide information about governing controls and maneuver constraints.The analysis assesses topology properties relevant to downstream use but does not directly evaluate a planner.Source: §E.3 · p. 22, §E.4 · p. 23

E.4 SCENE-LEVEL EGO-ROUTE RECOVERABILITY FOR NAVIGATION AND PLANNING

  • Among scenes with required lane detections detected and matched to ground truth, recoverability is the fraction whose complete actual ego route has every lane transition correctly recovered in the predicted scene-level topology graph.Source: Table 13 · p. 23
  • The route analysis aggregates frame-level predictions into scene-level lane graphs and evaluates routes only when their required lane detections are matched.The complete ego route typically spans 70–110 meters; a single missing or incorrect transition prevents route recovery.Source: §E.4 · p. 23, Table 13 · p. 23

E.5 EFFICIENCY ANALYSIS FOR REAL-TIME INFERENCE

TopoEnhance is evaluated for latency and memory overhead under a dense graph stress test. The reported inference overhead is below 10 ms, with a small additional memory requirement.

E.5 EFFICIENCY ANALYSIS FOR REAL-TIME INFERENCE

  • The stress test uses a nuScenes (Subset_B) scene with 200 lane nodes and 100 traffic-element nodes on one NVIDIA H200 GPU.This configuration yields 39,800 candidate lane–lane edges and 20,000 candidate lane–traffic edges.Source: §E.5 · pp. 23–24
  • 6.43 ms is the total inference overhead, including 5.20 ms for GNN message passing, 1.02 ms for edge decoding, and 0.21 ms for feature extraction.The GNN stage accounts for 80.9% of runtime, evaluating approximately 60,000 candidate edges.Source: §E.5 · p. 24, Table 14 · p. 24
  • TopoEnhance adds +0.3 MB of VRAM; GNN message passing adds +0.0 MB.The authors attribute the low memory overhead to optimized graph kernels, in-place operations, and processing high-level graph abstractions.Source: §E.5 · p. 24, Table 15 · p. 24

F LIMITATION AND FUTURE WORKS

Lane–traffic refinement depends on camera images, which limits its applicability to settings where images are available. Lane–lane refinement does not depend on image features and remains applicable without cameras.

F LIMITATION AND FUTURE WORKS

  • Camera-image availability constrains TopoEnhance’s lane–traffic refinement, but not its lane–lane refinement.The authors identify camera images as a source of semantic cues for distinguishing traffic signs and other traffic elements.Source: F LIMITATION AND FUTURE WORKS · pp. 24–25
  • Traffic-element semantic labels could replace images, but the authors note that such labels are typically unavailable at inference time and require another perception or annotation pipeline.Source: F LIMITATION AND FUTURE WORKS · p. 24

G ADDITIONAL QUALITATIVE RESULTS

The additional qualitative figures show predictions before and after TopoEnhance refinement across baselines and driving scenes. Figures 4 and 5 present each baseline against the same ground-truth view.

G ADDITIONAL QUALITATIVE RESULTS

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