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.

翻译：拓扑推理对于自动驾驶至关重要。现有方法主要聚焦于中心线检测的实例级学习，随后利用基于简化MLP层的顺序模块进行拓扑推理，且常忽视拓扑推理中\textit{点-实例}（P2I）关系的重要性。针对上述局限，我们提出TopoHR（拓扑层次化表示）——一种新颖的端到端框架，通过建立中心线检测与拓扑推理之间的循环交互机制，使二者能迭代互促。具体而言，我们引入包含点查询、实例查询和语义表示的层级中心线表示，这些多层次特征在层级中心线解码器中实现无缝集成与融合。进一步地，我们设计了一种层级拓扑推理模块，该模块在统一架构内同时捕获细粒度P2I关系与全局实例-实例（I2I）连接。凭借这些创新组件，TopoHR确保了拓扑推理的准确性与鲁棒性。在OpenLane-V2基准测试中，TopoHR以显著优势刷新了当前最优性能。值得注意的是，与先前最优结果相比，TopoHR在$\text{subset_A}$上实现$\mathrm{DET}_{\text{l}}$提升+3.8、$\mathrm{TOP}_{\text{ll}}$提升+5.4，在$\text{subset_B}$上实现$\mathrm{DET}_{\text{l}}$提升+11.0、$\mathrm{TOP}_{\text{ll}}$提升+7.9，验证了所提组件的有效性。代码将开源至https://github.com/Yifeng-Bai/TopoHR.git。