title: Sparse Laneformer
summary: Lane detection is a fundamental task in autonomous driving, and has achieved
  great progress as deep learning emerges. Previous anchor-based methods often
  design dense anchors, which highly depend on the training dataset and remain
  fixed during inference. We analyze that dense anchors are not necessary for
  lane detection, and propose a transformer-based lane detection framework based
  on a sparse anchor mechanism. To this end, we generate sparse anchors with
  position-aware lane queries and angle queries instead of traditional explicit
  anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane
  features along the horizontal direction, and adopt Lane-Angle Cross Attention
  (LACA) to perform interactions between lane queries and angle queries. We also
  propose Lane Perceptual Attention (LPA) based on deformable cross attention to
  further refine the lane predictions. Our method, named Sparse Laneformer, is
  easy-to-implement and end-to-end trainable. Extensive experiments demonstrate
  that Sparse Laneformer performs favorably against the state-of-the-art methods,
  e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score
  with fewer MACs on CULane with the same ResNet-34 backbone.
paper_authors._id: 66189b3e11548294cc9d0eb3
paper_authors.name: Ji Liu
paper_authors._id: 66189b3e11548294cc9d0eb4
paper_authors.name: Zifeng Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:36:39.676Z
paper_authors._id: 64fff28e2ad36636be7e3297
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64fff28e2ad36636be7e3297/uANi4XAn7LvlEb5-Nc835.jpeg
paper_authors.fullname: ZIFENG ZHANG
paper_authors.type: user
paper_authors.user: ConstStaticVoid
paper_authors._id: 66189b3e11548294cc9d0eb5
paper_authors.name: Mingjie Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:36:31.262Z
paper_authors._id: 6539d1322a25dcfb561a2031
paper_authors.avatarUrl: /avatars/d61e59d440741d3ee64df840f2b3d0fb.svg
paper_authors.fullname: MingjieLu
paper_authors.type: user
paper_authors.user: mingjiel
paper_authors._id: 66189b3e11548294cc9d0eb6
paper_authors.name: Hongyang Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:36:22.820Z
paper_authors._id: 6539d1bf43d9189cdce4b9cd
paper_authors.avatarUrl: /avatars/8a8dbcaba7501837915f4e5279e113cb.svg
paper_authors.fullname: Hongyang Wei
paper_authors.type: user
paper_authors.user: hongywei
paper_authors._id: 66189b3e11548294cc9d0eb7
paper_authors.name: Dong Li
paper_authors._id: 66189b3e11548294cc9d0eb8
paper_authors.name: Yile Xie
paper_authors._id: 66189b3e11548294cc9d0eb9
paper_authors.name: Jinzhang Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:37:09.906Z
paper_authors._id: 6539e31d7ba797097a946302
paper_authors.avatarUrl: /avatars/95f091ad3584a0abc95066658f4e7d52.svg
paper_authors.fullname: Jinzhang Peng
paper_authors.type: user
paper_authors.user: PengJinzhang
paper_authors._id: 66189b3e11548294cc9d0eba
paper_authors.name: Lu Tian
paper_authors._id: 66189b3e11548294cc9d0ebb
paper_authors.name: Ashish Sirasao
paper_authors._id: 66189b3e11548294cc9d0ebc
paper_authors.name: Emad Barsoum
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:37:37.693Z
paper_authors._id: 65adc9d086f88a686be41215
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65adc9d086f88a686be41215/xizVHuZPkE0Gu8_ulx0Fm.jpeg
paper_authors.fullname: Emad Barsoum
paper_authors.type: user
paper_authors.user: ebarsoum
paper_title: Sparse Laneformer
paper_summary: Lane detection is a fundamental task in autonomous driving, and has achieved
  great progress as deep learning emerges. Previous anchor-based methods often
  design dense anchors, which highly depend on the training dataset and remain
  fixed during inference. We analyze that dense anchors are not necessary for
  lane detection, and propose a transformer-based lane detection framework based
  on a sparse anchor mechanism. To this end, we generate sparse anchors with
  position-aware lane queries and angle queries instead of traditional explicit
  anchors. We adopt Horizontal Perceptual Attention (HPA) to aggregate the lane
  features along the horizontal direction, and adopt Lane-Angle Cross Attention
  (LACA) to perform interactions between lane queries and angle queries. We also
  propose Lane Perceptual Attention (LPA) based on deformable cross attention to
  further refine the lane predictions. Our method, named Sparse Laneformer, is
  easy-to-implement and end-to-end trainable. Extensive experiments demonstrate
  that Sparse Laneformer performs favorably against the state-of-the-art methods,
  e.g., surpassing Laneformer by 3.0% F1 score and O2SFormer by 0.7% F1 score
  with fewer MACs on CULane with the same ResNet-34 backbone.
paper_ai_keywords: transformer-based lane detection framework sparse performance mechanism position-aware lane queries angle queries Horizontal Perceptual Attention (HPA) Lane-Angle Cross Attention (LACA) Lane Perceptual Attention (LPA) deformable cross attention Sparse Laneformer CULane ResNet-34 F1 score MACs