Precise modeling of lane topology is essential for autonomous driving, as it directly impacts navigation and control decisions. Existing methods typically represent each lane with a single query and infer topological connectivity based on the similarity between lane queries. However, this kind of design struggles to accurately model complex lane structures, leading to unreliable topology prediction. In this view, we propose a Fine-Grained lane topology reasoning framework (TopoFG). It divides the procedure from bird's-eye-view (BEV) features to topology prediction via fine-grained queries into three phases, i.e., Hierarchical Prior Extractor (HPE), Region-Focused Decoder (RFD), and Robust Boundary-Point Topology Reasoning (RBTR). Specifically, HPE extracts global spatial priors from the BEV mask and local sequential priors from in-lane keypoint sequences to guide subsequent fine-grained query modeling. RFD constructs fine-grained queries by integrating the spatial and sequential priors. It then samples reference points in RoI regions of the mask and applies cross-attention with BEV features to refine the query representations of each lane. RBTR models lane connectivity based on boundary-point query features and further employs a topological denoising strategy to reduce matching ambiguity. By integrating spatial and sequential priors into fine-grained queries and applying a denoising strategy to boundary-point topology reasoning, our method precisely models complex lane structures and delivers trustworthy topology predictions. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoFG achieves new state-of-the-art performance, with an OLS of 48.0 on subsetA and 45.4 on subsetB.

翻译：车道拓扑的精确建模对于自动驾驶至关重要，因为它直接影响导航与控制决策。现有方法通常使用单一查询表示每条车道，并基于车道查询之间的相似性推断拓扑连接关系。然而，此类设计难以准确建模复杂的车道结构，导致拓扑预测不可靠。鉴于此，我们提出一种细粒度车道拓扑推理框架（TopoFG）。该框架通过细粒度查询将鸟瞰图特征至拓扑预测的过程划分为三个阶段：分层先验提取器、区域聚焦解码器与鲁棒边界点拓扑推理器。具体而言，分层先验提取器从鸟瞰图掩码中提取全局空间先验，并从车道内关键点序列中提取局部序列先验，以指导后续细粒度查询建模。区域聚焦解码器通过融合空间与序列先验构建细粒度查询，随后在掩码的感兴趣区域内采样参考点，并与鸟瞰图特征进行交叉注意力计算，以优化每条车道的查询表示。鲁棒边界点拓扑推理器基于边界点查询特征建模车道连接关系，并进一步采用拓扑去噪策略以降低匹配歧义。通过将空间与序列先验融入细粒度查询，并对边界点拓扑推理应用去噪策略，我们的方法能够精确建模复杂车道结构并提供可信的拓扑预测。在OpenLane-V2基准上的大量实验表明，TopoFG实现了新的最优性能，在子集A上获得48.0的OLS分数，在子集B上获得45.4的OLS分数。