DriveVA

[🎉ECCV 26] DriveVA: Video Action Models are Zero-Shot Drivers

Mengmeng Liu¹, Diankun Zhang², Jiuming Liu³,
Jianfeng Cui², Hongwei Xie², Guang Chen², Hangjun Ye²,
Michael Ying Yang⁴, Francesco Nex¹, Hao Cheng¹

¹University of Twente   ²Xiaomi EV   ³University of Cambridge   ⁴University of Bath

News

• July 20th, 2026: DriveVA NavSIM training code released.
• July 10th, 2026: DriveVA inference code and checkpoint released.
• June 20th, 2026: DriveVA is accepted by ECCV 2026🎉🎉🎉!
• Apr. 5th, 2026: DriveVA is released on arXiv.

Table of Contents

• News
• Table of Contents
• Introduction
• Qualitative Results
• Installation
• Model Preparation
• NavSIM v1 Training
• NavSIM v1 Evaluation
• nuScenes Evaluation
• Bench2Drive Evaluation
• All Datasets
• Configuration
• Acknowledgments
• Citation

Introduction

DriveVA investigates whether large video generation models can serve as generalizable video action models for autonomous driving. Instead of predicting future visual imagination and ego trajectories with loosely coupled modules, DriveVA jointly decodes future video latents and action tokens in a shared generative process. The model inherits motion and physical priors from pretrained video generation backbones, uses a DiT-based decoder for unified video-trajectory prediction, and applies video continuation for longer and more consistent rollouts.

DriveVA reports a 90.9 PDM score on NAVSIM and demonstrates zero-shot transfer to nuScenes and Bench2Drive, reducing average L2 error and collision rate over the prior world-model-based planner baseline reported in the paper.

Unified video-trajectory rollout for planning.

Overall pipeline of DriveVA.

Qualitative Results

Zero-shot video-trajectory consistency comparison on nuScenes.

Predicted video imaginations and corresponding trajectories.

Installation

Recommended environment:

• Linux
• Python 3.10
• CUDA-capable GPU
• Git

git clone https://github.com/xiaomi-mlab/DriveVA.git
cd DriveVA

export REPO_ROOT="$(pwd)"

apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0
python -m pip install "setuptools==60.2.0"
python -m pip install -e .

python -m pip install -e . does not install nuPlan, FlashAttention, or fetch external runtime repositories. Use the official repositories as installation references when these dependencies are needed:

• FlashAttention: https://github.com/Dao-AILab/flash-attention
• nuPlan devkit: https://github.com/motional/nuplan-devkit

This repository does not vendor the nuScenes devkit. Clone the official nuScenes devkit repository into third_party/nuscenes-devkit before running nuScenes inference:

git clone https://github.com/nutonomy/nuscenes-devkit.git third_party/nuscenes-devkit

The launchers add third_party/nuscenes-devkit/python-sdk to PYTHONPATH automatically after the directory exists.

Model Preparation

Download the Wan2.2 base model and DriveVA checkpoint to the default paths:

python -m pip install -U "huggingface_hub[cli]"

mkdir -p models/Wan-AI checkpoints

huggingface-cli download Wan-AI/Wan2.2-TI2V-5B \
  --local-dir models/Wan-AI/Wan2.2-TI2V-5B

huggingface-cli download mengmengliu1998/DriveVA DriveVA.safetensors \
  --local-dir checkpoints

mv checkpoints/DriveVA.safetensors checkpoints/pdms90_9.safetensors

The released DriveVA checkpoint is hosted at https://huggingface.co/mengmengliu1998/DriveVA and is saved as checkpoints/pdms90_9.safetensors by the commands above.

The Wan2.2 model directory should contain the text encoder, VAE, and diffusion checkpoint files required by the Wan2.2 TI2V 5B runtime. Override the defaults with LOCAL_MODEL_PATH and FULL_CKPT when needed.

NavSIM v1 Training

Set the NAVSIM v1 trainval paths and Wan2.2 model directory:

export NAVSIM_LOG_PATH=/path/to/navsim_logs/trainval
export SENSOR_BLOBS_PATH=/path/to/nuplan/sensor_blobs
export NUPLAN_MAPS_ROOT=/path/to/nuplan/maps
export NUPLAN_DATA_ROOT=/path/to/navsim/dataset
export LOCAL_MODEL_PATH=$PWD/models
export FULL_CKPT=$PWD/checkpoints/pdms90_9.safetensors

bash examples/wanvideo/driveva_train/scripts/train_dist_multi_node_navsim.sh

To evaluate saved checkpoints on NavSIM, nuScenes, and Bench2Drive:

bash examples/wanvideo/driveva_train/scripts/train_w_infer.sh

train_w_infer.sh watches OUTPUT_PATH and evaluates both raw and EMA checkpoints by default. Use EVAL_CKPT_KIND=ema or EVAL_CKPT_KIND=raw to evaluate only one checkpoint kind.

Common overrides include OUTPUT_PATH, NUM_EPOCHS, SAVE_STEPS, LR, NUM_HISTORY_FRAMES, NUM_FUTURE_FRAMES, TRAINABLE_MODELS, USE_EMA, SAVE_EMA, and SMOKE_TEST=1.

NavSIM v1 Evaluation

Set the dataset paths, NuPlan paths, and checkpoint:

export NAVSIM_LOG_PATH=/path/to/navsim_logs/test
export NAVSIM_SENSOR_BLOBS_PATH=/path/to/sensor_blobs/test
export NAVSIM_METRIC_CACHE_PATH=/path/to/metric_cache
export NUPLAN_MAPS_ROOT=/path/to/nuplan/maps
export NUPLAN_DATA_ROOT=/path/to/navsim/dataset
export FULL_CKPT=$PWD/checkpoints/pdms90_9.safetensors

NAVSIM_METRIC_CACHE_PATH should point to metric caches generated with the official NAVSIM v1.1 metric-caching workflow. See the NAVSIM v1.1 branch and scripts/evaluation/run_metric_caching.sh for the reference command:

• https://github.com/autonomousvision/navsim/tree/v1.1
• https://github.com/autonomousvision/navsim/blob/v1.1/scripts/evaluation/run_metric_caching.sh

The official v1.1 script generates the cache with this pattern:

TRAIN_TEST_SPLIT=navtest
CACHE_PATH=$NAVSIM_EXP_ROOT/metric_cache
python $NAVSIM_DEVKIT_ROOT/navsim/planning/script/run_metric_caching.py \
  train_test_split=$TRAIN_TEST_SPLIT \
  cache.cache_path=$CACHE_PATH

Run evaluation:

bash examples/wanvideo/driveva_infer/scripts/eval_navsim_v1.sh

Small check:

export SMOKE_TEST=1
export MAX_EVAL_TOKENS=64
bash examples/wanvideo/driveva_infer/scripts/eval_navsim_v1.sh

Common overrides include NUM_INFERENCE_STEPS, CFG_SCALE, SEED, MAX_EVAL_TOKENS, SAVE_VIZ, HEIGHT, WIDTH, TRAJECTORY_CONDITION_MODE, TRAFFIC_AGENTS_POLICY, SCENE_FILTER_YAML, and NUM_EVAL_SHARDS.

nuScenes Evaluation

export NUSCENES_DATAROOT=/path/to/nuscenes
export FULL_CKPT=$PWD/checkpoints/pdms90_9.safetensors
export LOCAL_MODEL_PATH=$PWD/models

bash examples/wanvideo/driveva_infer/scripts/infer_nuscenes.sh

Set NUSCENES_EVAL_POLICY_ANNO_JSON when you need the same validation token filter as a reference run. If the file is not found, the launcher evaluates the unfiltered nuScenes validation split.

Bench2Drive Evaluation

export B2D_DATA_ROOT=/path/to/bench2drive
export B2D_ANN_FILE=/path/to/b2d_infos_val.pkl
export FULL_CKPT=$PWD/checkpoints/pdms90_9.safetensors

bash examples/wanvideo/driveva_infer/scripts/infer_bench2drive.sh

For the common repository layout, the defaults are:

B2D_DATA_ROOT=$REPO_ROOT/data/bench2drive
B2D_ANN_FILE=$REPO_ROOT/data/infos/b2d_infos_val.pkl
OUTPUT_DIR=$REPO_ROOT/outputs/bench2drive

The data/infos/*.pkl annotation files are expected to be processed metadata from ORION's Bench2Drive preprocessing results. Use the official Bench2Drive repository and ORION repository as references for the dataset layout and processed info files:

• Bench2Drive: https://github.com/Thinklab-SJTU/Bench2Drive
• ORION: https://github.com/xiaomi-mlab/Orion

All Datasets

Run all three launchers from one entry point:

export FULL_CKPT=$PWD/checkpoints/pdms90_9.safetensors
export LOCAL_MODEL_PATH=$PWD/models

bash examples/wanvideo/driveva_infer/scripts/infer_all.sh

infer_all.sh runs NavSIM, nuScenes, and Bench2Drive sequentially by default and writes to outputs/all_infer/{navsim_v1,nuscenes,bench2drive}. Select datasets with RUN_NAVSIM=0, RUN_NUSCENES=0, or RUN_B2D=0. Per-dataset overrides include NAVSIM_OUTPUT_DIR, NUSCENES_OUTPUT_DIR, B2D_OUTPUT_DIR, NAVSIM_CONFIG, NUSCENES_CONFIG, and B2D_CONFIG.

To run them in parallel, set INFER_ALL_MODE=parallel and assign GPUs per dataset, for example:

export NAVSIM_CUDA_VISIBLE_DEVICES=0
export NUSCENES_CUDA_VISIBLE_DEVICES=1
export B2D_CUDA_VISIBLE_DEVICES=2
INFER_ALL_MODE=parallel bash examples/wanvideo/driveva_infer/scripts/infer_all.sh

Configuration

Launchers read defaults from examples/wanvideo/driveva_infer/configs/. Use CONFIG=/path/to/config.yaml for another config file. Distributed execution is inferred from standard environment variables such as CUDA_VISIBLE_DEVICES, GPUS, GPUS_PER_NODE, NNODES, RANK, NODE_RANK, MASTER_ADDR, and MASTER_PORT.

Acknowledgments

DriveVA builds on and is inspired by the following open-source projects and benchmarks: Wan2.2, NAVSIM, nuScenes, nuPlan, Bench2Drive, FlashAttention, and Diffusers.

• DiffSynth-Studio: https://github.com/modelscope/DiffSynth-Studio
• Wan2.2: https://github.com/Wan-Video/Wan2.2
• NAVSIM: https://github.com/autonomousvision/navsim
• nuScenes devkit: https://github.com/nutonomy/nuscenes-devkit
• nuPlan devkit: https://github.com/motional/nuplan-devkit
• Bench2Drive: https://github.com/Thinklab-SJTU/Bench2Drive
• FlashAttention: https://github.com/Dao-AILab/flash-attention
• Diffusers: https://github.com/huggingface/diffusers

Citation

If DriveVA is useful for your research or applications, please consider citing:

@article{liu2026driveva,
  title={DriveVA: Video Action Models are Zero-Shot Drivers},
  author={Liu, Mengmeng and Zhang, Diankun and Liu, Jiuming and Cui, Jianfeng and Xie, Hongwei and Chen, Guang and Ye, Hangjun and Yang, Michael Ying and Nex, Francesco and Cheng, Hao},
  journal={arXiv preprint arXiv:2604.04198},
  year={2026}
}