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TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
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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.
Forward citations
Cited by 6 Pith papers
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Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning
UniTopo unifies lane detection and topology reasoning into a single perception model, outperforming prior methods on OpenLane-V2 benchmarks with TOP_ll scores of 30.1% and 31.8%.
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TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
TopoHR proposes a hierarchical centerline representation and topology reasoning module with point-to-instance relations and cyclic interactions, achieving new state-of-the-art results on the OpenLane-V2 benchmark for ...
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Unified Map Prior Encoder for Mapping and Planning
UMPE fuses any subset of HD/SD vector maps, raster SD maps, and satellite imagery into BEV features via alignment-aware vector and raster branches, raising mapping mAP by 5.3-5.9 points and cutting planning L2 error b...
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TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
TopoHR introduces hierarchical point/instance/semantic queries and a unified P2I+I2I topology module that reports SOTA gains on OpenLane-V2 subsets.
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Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding
CoT data curated by two-round LLM prompting and VLM verification, then SFT+GRPO with fine-grained rewards, improves MapDR rule–lane association F1 from 0.642 to 0.723.
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Reusing Attention for One-stage Lane Topology Understanding
A one-stage transformer with attention reuse predicts lane and traffic-element topology directly, improving accuracy and speed on OpenLane-V2.
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