DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
  Driving
Authors: name: Bencheng Liao, name: Shaoyu Chen, name: Haoran Yin, name: Bo Jiang, name: Cheng Wang, name: Sixu Yan, name: Xinbang Zhang, name: Xiangyu Li, name: Ying Zhang, name: Qian Zhang, name: Xinggang Wang

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.