Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane topology, and lane-traffic topology. In this work, we first present that the topology score relies heavily on detection performance on lane and traffic elements. Therefore, we introduce a powerful 3D lane detector and an improved 2D traffic element detector to extend the upper limit of topology performance. Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning. Based on the impressive detection performance, we develop two simple MLP-based heads for topology generation. TopoMLP achieves state-of-the-art performance on OpenLane-V2 benchmark, i.e., 41.2% OLS with ResNet-50 backbone. It is also the 1st solution for 1st OpenLane Topology in Autonomous Driving Challenge. We hope such simple and strong pipeline can provide some new insights to the community. Code is at https://github.com/wudongming97/TopoMLP.

翻译：拓扑推理旨在全面理解道路场景并在自动驾驶中呈现可行驶路径。它需要检测道路中心线（车道线）和交通元素，并进一步推理其拓扑关系，即车道-车道拓扑和车道-交通拓扑。在本文中，我们首先指出拓扑得分高度依赖于车道线和交通元素的检测性能。因此，我们引入了一个强大的3D车道检测器和一个改进的2D交通元素检测器，以扩展拓扑性能的上限。此外，我们提出了TopoMLP，一种简单而高性能的驾驶拓扑推理流程。基于出色的检测性能，我们开发了两个基于MLP的简单头模块用于拓扑生成。TopoMLP在OpenLane-V2基准上达到了最先进的性能，即使用ResNet-50骨干网络时OLS为41.2%。它也是第一届OpenLane拓扑自动驾驶挑战赛的冠军解决方案。我们希望这种简单而强大的流程能为该领域提供一些新的见解。代码开源地址：https://github.com/wudongming97/TopoMLP。