发表机构
Zhejiang University of Science and Technology; Hunan University; Karlsruhe Institute of Technology（浙江科技学院; 湖南大学; 卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实环境中心线检测难题，提出HGeo-TopoMap，利用显式先验地图和隐式空间关系，设计几何自适应和一致性学习模块增强拓扑映射，在OpenLane-V2数据集上评估，提高了精度与鲁棒性，优于基线方法。
AI 中文摘要
拓扑地图是自动驾驶感知系统的关键输出，为路径规划提供重要道路信息。由于现实环境中缺少中心线的明确标记，中心线实例检测仍是重大挑战。为此提出HGeo-TopoMap，利用显式先验地图和隐式空间关系分层增强拓扑映射。设计几何自适应学习模块对逆透视映射得到的道路结构图进行处理，离散编码语义和空间特征，通过先验掩码注意力机制聚焦信息区域。还设计几何一致性学习模块，利用中心线几何属性和空间关系，基于几何感知解码器通过对齐相同几何方向的中心线实例特征来增强空间一致性。在OpenLane-V2数据集上评估，该方法大幅提高拓扑映射精度，增强了鲁棒性，优于基线方法。源代码和模型权重将公开。
英文摘要
Topological maps are key outputs of autonomous driving perception systems, delivering essential road information for path planning. They identify instances such as centerlines and traffic signs, along with their connectivity relationships. Due to the lack of explicit markings for centerlines in real-world environments, the detection of centerline instances remains a significant challenge. To tackle this problem, we propose HGeo-TopoMap, which leverages an explicit prior map and implicit spatial relations to hierarchically boost topological mapping. First, a geometric adaptive learning module is designed for the road structure map obtained via inverse perspective mapping. This module discretely encodes semantic and spatial features from the map, followed by a prior-mask attention mechanism that selectively focuses on informative regions. Then, a geometric consistency learning module is devised, which leverages the geometric properties and spatial relationships of centerlines. Built on the geometry-aware decoder, it enforces spatial consistency by aligning features of centerline instances with identical geometric orientations. The proposed method is evaluated on the OpenLane-V2 dataset across the centerline, lane segment, and robustness benchmarks. Beyond substantial improvements in topological mapping accuracy, the proposed method offers the benefit of enhanced robustness, consistently outperforming baselines under both standard and challenging conditions. The source code and model weights will be made publicly available at https://github.com/lynn-yu/HGeo-TopoMap.
CommentsThe source code and model weights will be made publicly available at https://github.com/lynn-yu/HGeo-TopoMap
