TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations

2026-04-27

AI Summary

Existing approaches to autonomous driving topology reasoning typically decouple centerline detection from topological inference and neglect point-to-instance (P2I) relationships, leading to inaccurate topological modeling. This work proposes TopoHR, an end-to-end hierarchical framework that unifies the modeling of P2I and inter-instance relationships through a hierarchical query mechanism involving point queries, instance queries, and semantic representations. By enabling iterative interactions between detection and reasoning modules, TopoHR achieves closed-loop co-optimization. Evaluated on the OpenLane-V2 benchmark, TopoHR demonstrates substantial improvements: on subset_A, it achieves a 3.8-point gain in DETₗ and a 5.4-point gain in TOPₗₗ; on subset_B, it improves DETₗ by 11.0 points and TOPₗₗ by 7.9 points.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Topology reasoning is crucial for autonomous driving. Current methods primarily focus on instance-level learning for centerline detection, followed by a sequential module for topology reasoning that relies on simplified MLP layers. Moreover, they often neglect the importance of \textit{point-to-instance} (P2I) relationships in topology reasoning. To address these limitations, we present TopoHR (Topological Hierarchical Representation), a novel end-to-end framework that establishes cyclic interaction between centerline detection and topology reasoning, allowing them to iteratively enhance each other. Specifically, we introduce a hierarchical centerline representation including point queries, instance queries, and semantic representations. These multi-level features are seamlessly integrated and fused within a hierarchical centerline decoder. Furthermore, we design a hierarchical topology reasoning module that captures both fine-grained P2I relationships and global instance-to-instance (I2I) connections within a unified architecture. With these novel components, TopoHR ensures accurate and robust topology reasoning. On the OpenLane-V2 benchmark, TopoHR refreshes state-of-the-art performance with significant improvements. Notably, compared with previous best results, TopoHR achieves +3.8 in $\mathrm{DET}_{\text{l}}$, +5.4 in $\mathrm{TOP}_{\text{ll}}$ on $\text{subset_A}$ and +11.0 in $\mathrm{DET}_{\text{l}}$, +7.9 in $\mathrm{TOP}_{\text{ll}}$ on $\text{subset_B}$, validating the effectiveness of the proposed components. The code will be shared publicly at https://github.com/Yifeng-Bai/TopoHR.git.

Problem

• topology reasoning
• centerline detection
• point-to-instance relations
• autonomous driving
• cyclic topology

Innovation

• hierarchical representation
• cyclic topology reasoning
• point-to-instance relations
• centerline detection
• end-to-end framework