Learning Lane-Structured Conditioning Fields for Online HD Mapping

Key points

• High-definition maps provide critical structural information for downstream autonomous driving tasks.1
• Yet, existing prior representations expose a trade-off: dense rasters are easy to integrate but ignore certain map geometric properties, while sparse vectors preserve geometric structure but require dedicated encoders and are difficult to plug into existing architectures.1
• We address this limitation with the Lane Field, a dense BEV-aligned geometric representation that encodes local lane structure at each spatial location and can be either predicted from onboard observations or constructed from external map priors.1
• We primarily evaluate our method on OpenLane-V2, showing that conditioning BEV features and guiding deformable attention with local lane geometry enables our framework to consistently improve existing methods and achieve state-of-the-art performance on lane segment perception.1

Sources (1)

• [1]Learning Lane-Structured Conditioning Fields for Online HD Mapping
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 02:04 PM

  Building on this representation, we introduce a plug-and-play framework that enhances online HD mapping through two complementary mechanisms: BEV feature modulation, which injects lane-structured cues into latent BEV features, and deformable attention guidance, which steers query sampling toward the underlying lane geometry.

  High-definition maps provide critical structural information for downstream autonomous driving tasks.

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