title: DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
    Driving
summary: Recently, the diffusion model has emerged as a powerful generative technique
  for robotic policy learning, capable of modeling multi-mode action
  distributions. Leveraging its capability for end-to-end autonomous driving is a
  promising direction. However, the numerous denoising steps in the robotic
  diffusion policy and the more dynamic, open-world nature of traffic scenes pose
  substantial challenges for generating diverse driving actions at a real-time
  speed. To address these challenges, we propose a novel truncated diffusion
  policy that incorporates prior multi-mode anchors and truncates the diffusion
  schedule, enabling the model to learn denoising from anchored Gaussian
  distribution to the multi-mode driving action distribution. Additionally, we
  design an efficient cascade diffusion decoder for enhanced interaction with
  conditional scene context. The proposed model, DiffusionDrive, demonstrates
  10times reduction in denoising steps compared to vanilla diffusion policy,
  delivering superior diversity and quality in just 2 steps. On the
  planning-oriented NAVSIM dataset, with the aligned ResNet-34 backbone,
  DiffusionDrive achieves 88.1 PDMS without bells and whistles, setting a new
  record, while running at a real-time speed of 45 FPS on an NVIDIA 4090.
  Qualitative results on challenging scenarios further confirm that
  DiffusionDrive can robustly generate diverse plausible driving actions. Code
  and model will be available at https://github.com/hustvl/DiffusionDrive.
paper_authors._id: 6747e0c3e2d14d26680251b1
paper_authors.name: Bencheng Liao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-28T09:19:40.481Z
paper_authors._id: 6577073fc2bf55b1f6bafb49
paper_authors.avatarUrl: /avatars/58803398b1a918b7570db17893e65122.svg
paper_authors.fullname: liao
paper_authors.type: user
paper_authors.user: LegendBC
paper_authors._id: 6747e0c3e2d14d26680251b2
paper_authors.name: Shaoyu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T10:45:55.020Z
paper_authors._id: 64c1c9d595181d492cd90531
paper_authors.avatarUrl: /avatars/b686706e661f22f0472ef716838946ea.svg
paper_authors.fullname: Shaoyu Chen
paper_authors.type: user
paper_authors.user: csy71
paper_authors._id: 6747e0c3e2d14d26680251b3
paper_authors.name: Haoran Yin
paper_authors._id: 6747e0c3e2d14d26680251b4
paper_authors.name: Bo Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T10:46:16.062Z
paper_authors._id: 6505b82fa61b2c010ee14c7b
paper_authors.avatarUrl: /avatars/a092795b5fdd566c072fe05b20732b23.svg
paper_authors.fullname: Bo JIANG
paper_authors.type: user
paper_authors.user: bojiang-bentoml
paper_authors._id: 6747e0c3e2d14d26680251b5
paper_authors.name: Cheng Wang
paper_authors._id: 6747e0c3e2d14d26680251b6
paper_authors.name: Sixu Yan
paper_authors._id: 6747e0c3e2d14d26680251b7
paper_authors.name: Xinbang Zhang
paper_authors._id: 6747e0c3e2d14d26680251b8
paper_authors.name: Xiangyu Li
paper_authors._id: 6747e0c3e2d14d26680251b9
paper_authors.name: Ying Zhang
paper_authors._id: 6747e0c3e2d14d26680251ba
paper_authors.name: Qian Zhang
paper_authors._id: 6747e0c3e2d14d26680251bb
paper_authors.name: Xinggang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T10:43:58.649Z
paper_authors._id: 62600de6d47e3dbae32ce1ce
paper_authors.avatarUrl: /avatars/a536417cfec6e10ac415091bd1829426.svg
paper_authors.fullname: Xinggang Wang
paper_authors.type: user
paper_authors.user: xinggangw
paper_title: DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
    Driving
paper_summary: Recently, the diffusion model has emerged as a powerful generative technique
  for robotic policy learning, capable of modeling multi-mode action
  distributions. Leveraging its capability for end-to-end autonomous driving is a
  promising direction. However, the numerous denoising steps in the robotic
  diffusion policy and the more dynamic, open-world nature of traffic scenes pose
  substantial challenges for generating diverse driving actions at a real-time
  speed. To address these challenges, we propose a novel truncated diffusion
  policy that incorporates prior multi-mode anchors and truncates the diffusion
  schedule, enabling the model to learn denoising from anchored Gaussian
  distribution to the multi-mode driving action distribution. Additionally, we
  design an efficient cascade diffusion decoder for enhanced interaction with
  conditional scene context. The proposed model, DiffusionDrive, demonstrates
  10times reduction in denoising steps compared to vanilla diffusion policy,
  delivering superior diversity and quality in just 2 steps. On the
  planning-oriented NAVSIM dataset, with the aligned ResNet-34 backbone,
  DiffusionDrive achieves 88.1 PDMS without bells and whistles, setting a new
  record, while running at a real-time speed of 45 FPS on an NVIDIA 4090.
  Qualitative results on challenging scenarios further confirm that
  DiffusionDrive can robustly generate diverse plausible driving actions. Code
  and model will be available at https://github.com/hustvl/DiffusionDrive.
paper_ai_summary: A truncated diffusion policy with a cascade decoder improves real-time robustness and diversity in robotic policy learning for autonomous driving.
paper_ai_keywords: diffusion model robotic policy learning multi-mode action distributions denoising steps open-world nature truncated diffusion policy multi-mode anchors diffusion schedule anchored Gaussian distribution cascade diffusion decoder DiffusionDrive real-time speed NAVSIM dataset ResNet-34 backbone PDMS FPS