Topology reasoning aims to comprehensively understand road scenes and presentdrivable 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 firstpresent that the topology score relies heavily on detection performance on laneand traffic elements. Therefore, we introduce a powerful 3D lane detector andan improved 2D traffic element detector to extend the upper limit of topologyperformance. Further, we propose TopoMLP, a simple yet high-performancepipeline for driving topology reasoning. Based on the impressive detectionperformance, 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 OpenLaneTopology in Autonomous Driving Challenge. We hope such simple and strongpipeline can provide some new insights to the community. Code is athttps://github.com/wudongming97/TopoMLP.