Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code would be released soon.

翻译：理解道路基因组是实现自动驾驶的关键。这一高度智能化的问题包含两个方面——车道之间的连接关系，以及车道与交通要素之间的分配关系，但目前尚缺乏全面的拓扑结构推理方法。一方面，以往的制图学习技术难以通过分割或车道线范式推导车道连通性；而现有的车道拓扑导向方法主要关注中心线检测，忽略了交互建模。另一方面，交通要素到车道的分配问题局限于图像领域，如何构建两个视角之间的对应关系仍是一个未被探索的挑战。为解决这些问题，我们提出了TopoNet，这是首个能够超越传统感知任务抽取交通知识的端到端框架。为捕捉驾驶场景拓扑结构，我们引入了三个关键设计：（1）一个嵌入模块，将来自2D要素的语义知识整合到统一特征空间；（2）一个精心设计的场景图神经网络，用于建模关系并实现网络内部的特征交互；（3）不同于任意传递信息，我们设计了一个场景知识图谱，用于区分道路基因组中各类先验知识。我们在具有挑战性的场景理解基准OpenLane-V2上评估了TopoNet，我们的方法在所有感知和拓扑指标上大幅超越了所有先前工作。代码将很快发布。