3D lane detection and topology reasoning are essential tasks in autonomous driving scenarios, requiring not only detecting the accurate 3D coordinates on lane lines, but also reasoning the relationship between lanes and traffic elements. Current vision-based methods, whether explicitly constructing BEV features or not, all establish the lane anchors/queries in 3D space while ignoring the 2D lane priors. In this study, we propose Topo2D, a novel framework based on Transformer, leveraging 2D lane instances to initialize 3D queries and 3D positional embeddings. Furthermore, we explicitly incorporate 2D lane features into the recognition of topology relationships among lane centerlines and between lane centerlines and traffic elements. Topo2D achieves 44.5% OLS on multi-view topology reasoning benchmark OpenLane-V2 and 62.6% F-Socre on single-view 3D lane detection benchmark OpenLane, exceeding the performance of existing state-of-the-art methods.

翻译：3D车道检测与拓扑推理是自动驾驶场景中的关键任务，不仅需要准确检测车道线的3D坐标，还需推理车道与交通元素之间的拓扑关系。当前基于视觉的方法，无论是否显式构建BEV特征，均在3D空间中建立车道锚点/查询，而忽略了2D车道先验。本研究提出基于Transformer的新型框架Topo2D，利用2D车道实例初始化3D查询与3D位置编码。此外，我们显式地将2D车道特征融入车道中心线之间以及车道中心线与交通元素之间的拓扑关系识别中。Topo2D在多视角拓扑推理基准OpenLane-V2上达到44.5%的OLS，在单视角3D车道检测基准OpenLane上达到62.6%的F-Score，超越了现有最优方法的性能。