DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous
  Driving

Bencheng Liao; Shaoyu Chen; Haoran Yin; Bo Jiang; Cheng Wang; Sixu Yan; Xinbang Zhang; Xiangyu Li; Ying Zhang; Qian Zhang; Xinggang Wang

Paper · arXiv 2411.15139

DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving

Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, Xinggang Wang

15 upvotesNovember 22, 2024arXiv 预印本

AI 摘要

A truncated diffusion policy with a cascade decoder improves real-time robustness and diversity in robotic policy learning for autonomous driving.

diffusion modelrobotic policy learningmulti-mode action distributionsdenoising stepsopen-world naturetruncated diffusion policymulti-mode anchorsdiffusion scheduleanchored Gaussian distributioncascade diffusion decoderDiffusionDrivereal-time speedNAVSIM datasetResNet-34 backbonePDMSFPS

Abstract

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.