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

2026-10-07

AI Summary

This study addresses the theoretical performance gap in topological connectivity reasoning for autonomous driving, where existing threshold-based methods yield unreliable and logically inconsistent decision graphs. To overcome this, we propose TopoEnhance, a framework that formulates topological enhancement as a denoising process. By integrating denoising diffusion models, stochastic corruption reconstruction, and discrete topological optimization, the framework restores structural consistency and effectively resolves logical conflicts through denoised reconstruction. Functioning as a source-agnostic, plug-and-play module, TopoEnhance significantly boosts the performance of multiple state-of-the-art baselines without requiring retraining. It achieves substantial improvements in both the continuous TOP score and the discrete TJS metric, pushing topological reliability toward its theoretical upper bound.

Abstract

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.

Problem

• scene topology
• connectivity reasoning
• topology graph reliability
• autonomous driving
• discrete connectivity

Innovation

• Topology Enhancement
• Denoising-based Reconstruction
• Discrete Topology Graphs
• Source-agnostic Framework
• Autonomous Driving