Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors

• URL: http://arxiv.org/abs/2406.03105v1
• Date: Wed, 5 Jun 2024 09:48:56 GMT
• Title: Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane Priors
• Authors: Han Li, Zehao Huang, Zitian Wang, Wenge Rong, Naiyan Wang, Si Liu,
• Abstract summary: 3D lane detection and topology reasoning are essential tasks in autonomous driving scenarios. We propose Topo2D, a novel framework based on Transformer, leveraging 2D lane instances to initialize 3D queries and 3D positional embeddings. 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.
• Score: 40.92232275558338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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