DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
  Driving
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.
6747e0c3e2d14d26680251b1
Bencheng Liao
claimed_verified
2024-11-28T09:19:40.481Z
6577073fc2bf55b1f6bafb49
/avatars/58803398b1a918b7570db17893e65122.svg
Bencheng liao
user
LegendBC
6747e0c3e2d14d26680251b2
Shaoyu Chen
admin_assigned
2024-11-28T10:45:55.020Z
64c1c9d595181d492cd90531
/avatars/b686706e661f22f0472ef716838946ea.svg
Shaoyu Chen
user
csy71
6747e0c3e2d14d26680251b3
Haoran Yin
6747e0c3e2d14d26680251b4
Bo Jiang
admin_assigned
2024-11-28T10:46:16.062Z
6505b82fa61b2c010ee14c7b
/avatars/a092795b5fdd566c072fe05b20732b23.svg
Bo JIANG
user
bojiang-bentoml
6747e0c3e2d14d26680251b5
Cheng Wang
6747e0c3e2d14d26680251b6
Sixu Yan
6747e0c3e2d14d26680251b7
Xinbang Zhang
6747e0c3e2d14d26680251b8
Xiangyu Li
6747e0c3e2d14d26680251b9
Ying Zhang
6747e0c3e2d14d26680251ba
Qian Zhang
6747e0c3e2d14d26680251bb
Xinggang Wang
admin_assigned
2024-11-28T10:43:58.649Z
62600de6d47e3dbae32ce1ce
/avatars/a536417cfec6e10ac415091bd1829426.svg
Xinggang Wang
user
xinggangw
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
  Driving
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.
A truncated diffusion policy with a cascade decoder improves real-time robustness and diversity in robotic policy learning for autonomous driving.
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