# hustvl/DiffusionDrive

[CVPR 2025 Highlight] Truncated Diffusion Model for Real-Time End-to-End Autonomous Driving

Repository: https://github.com/hustvl/DiffusionDrive
Canonical: https://ross.abutalabs.com/products/diffusiondrive
Homepage: https://arxiv.org/abs/2411.15139
Language: Python
License: MIT
License Family: permissive
Last push: 2025-12-08T08:31:06+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 56, release rhythm 28, longevity 46
- inputs: {"age_days": 656, "days_push": 268, "days_rel": 592, "gap_med": 33, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1480, forks 145 (observed 2026-08-28T04:04:50.817396+00:00)

## What it is
DiffusionDrive is a truncated diffusion model for real-time end-to-end autonomous driving, released as the official PyTorch implementation of a CVPR 2025 Highlight paper from HUST and Horizon Robotics. It achieves record-breaking 88.1 PDMS on NAVSIM with a ResNet-34 backbone while running at 45 FPS using only 2 denoising steps.

## Use cases
- train an end-to-end driving policy on NAVSIM
- evaluate a diffusion-based planner on nuScenes
- generate diverse multimodal driving trajectories in real time
- reproduce CVPR 2025 autonomous driving benchmark results
- compare truncated diffusion policy against vanilla diffusion policy
- build a real-time motion planner for autonomous vehicles

## When to choose
- you need a fast, multimodal end-to-end driving policy model
- you are benchmarking on NAVSIM or nuScenes
- you want to research diffusion-based planning for robotics or driving

## When to avoid
- you need a production-ready autonomous driving stack
- you lack GPU resources for training diffusion models
- you need a general-purpose diffusion image generator

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, simulation
- domain: autonomous-vehicles, deep-learning, computer-vision, machine-learning
- platform: python
- tags: diffusion-model, autonomous-driving, end-to-end-driving, navsim, nuscenes, cvpr-2025, motion-planning, research-code, gpu

## Member repositories
- hustvl/DiffusionDrive (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:50.817396+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:34:14.906985+00:00, confidence not recorded.
  - readme: https://github.com/hustvl/DiffusionDrive (fetched 2026-08-28T04:04:50.817396+00:00, sha 53b2f27ea35d)
  - homepage: https://arxiv.org/abs/2411.15139 (fetched 2026-08-29T11:41:13.614307+00:00, sha 338d49dc8873)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T11:41:13.624474+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T11:41:13.628296+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T11:41:13.630825+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T11:41:13.626416+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.