[CVPR 2025 Highlight] Truncated Diffusion Model for Real-Time End-to-End Autonomous Driving
[CVPR 2025 Highlight] Truncated Diffusion Model for Real-Time End-to-End Autonomous Driving
Apr. 4th, 2025: DiffusionDrive is awarded as CVPR 2025 Highlight! Feb. 27th, 2025: DiffusionDrive is accepted to CVPR 2025! Jan. 18th, 2025: We release the initial version of code and weight on nuScenes, along with documentation and training/evaluation scripts. Please run git checkout nusc to use it. Dec. 16th, 2024: We release the initial version of code and weight on NAVSIM, along with documentation and training/evaluation scripts. Nov. 25th, 2024: We released our paper on Arxiv. Code/Models are coming soon. Please stay tuned! ☕️Diffusion policy exhibits promising multimodal property and distributional expressivity in robotic field, while not ready for real-time end-to-end autonomous driving in more dynamic and open-world traffic scenes. To bridge this gap, we propose a novel truncated diffusion model, DiffusionDrive, for real-time end-to-end autonomous driving, which is much faster (10x reduction in diffusion denoising steps), more accurate (3.5 higher PDMS on NAVSIM), and more diverse (64% higher mode diversity score) than the vanilla diffusion policy. Without bells and whistles, DiffusionDrive achieves record-breaking 88.1 PDMS on NAVSIM benchmark with the same ResNet-34 backbone by directly learning from human demonstrations, while running at a real-time speed of 45 FPS.
https://github.com/user-attachments/assets/bd2364f3-73fd-4c29-b8b2-ead11f78926d
Results on NAVSIM
| Method | Model Size | Backbone | PDMS | Weight Download |
|---|---|---|---|---|
| DiffusionDrive | 60M | ResNet-34 | 88.1 | Hugging Face |
Results on nuScenes
| Method | Backbone | Weight | Log | L2 (m) 1s | L2 (m) 2s | L2 (m) 3s | L2 (m) Avg | Col. (%) 1s | Col. (%) 2s | Col. (%) 3s | Col. (%) Avg |
|---|---|---|---|---|---|---|---|---|---|---|---|
| DiffusionDrive | ResNet-50 | HF | Github | 0.27 | 0.54 | 0.90 | 0.57 | 0.03 | 0.05 | 0.16 | 0.08 |
If you have any questions, please contact Bencheng Liao via email ([email protected]).
DiffusionDrive is greatly inspired by the following outstanding contributions to the open-source community: NAVSIM, Transfuser, Diffusion Policy, MapTR, VAD, SparseDrive.
If you find DiffusionDrive is useful in your research or applications, please consider giving us a star and citing it by the following BibTeX entry.
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