/GenAD

GenAD: Generative End-to-End Autonomous Driving

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GenAD: Generative End-to-End Autonomous Driving

Paper

GenAD: Generative End-to-End Autonomous Driving

Wenzhao Zheng*, Ruiqi Song*, Xianda Guo* $\dagger$, Chenming Zhang, Long Chen$\dagger$

* Equal contributions $\dagger$ Corresponding authors

GenAD casts autonomous driving as a generative modeling problem.

News

  • [2024/5/2] Training and evaluation code release.
  • [2024/2/18] Paper released on arXiv.

Demo

demo

Overview

comparison

Comparisons of the proposed generative end-to-end autonomous driving framework with the conventional pipeline. Most existing methods follow a serial design of perception, prediction, and planning. They usually ignore the high-level interactions between the ego car and other agents and the structural prior of realistic trajectories. We model autonomous driving as a future generation problem and conduct motion prediction and ego planning simultaneously in a structural latent trajectory space.

Results

results

Code

Dataset

Download nuScenes V1.0 full dataset data and CAN bus expansion data HERE. Prepare nuscenes data as follows.

Download CAN bus expansion

# download 'can_bus.zip'
unzip can_bus.zip 
# move can_bus to data dir

Prepare nuScenes data

We genetate custom annotation files which are different from mmdet3d's

Generate the train file and val file:

python tools/data_converter/genad_nuscenes_converter.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag genad_nuscenes --version v1.0 --canbus ./data

Using the above code will generate genad_nuscenes_infos_temporal_{train,val}.pkl.

Folder structure

GenAD
├── projects/
├── tools/
├── configs/
├── ckpts/
│   ├── resnet50-19c8e357.pth
├── data/
│   ├── can_bus/
│   ├── nuscenes/
│   │   ├── maps/
│   │   ├── samples/
│   │   ├── sweeps/
│   │   ├── v1.0-test/
|   |   ├── v1.0-trainval/
|   |   ├── genad_nuscenes_infos_train.pkl
|   |   ├── genad_nuscenes_infos_val.pkl

installation

Detailed package versions can be found in requirements.txt.

Getting Started

Train GenAD with 8 GPUs

cd /path/to/GenAD
conda activate genad
python -m torch.distributed.run --nproc_per_node=8 --master_port=2333 tools/train.py projects/configs/GenAD/GenAD_config.py --launcher pytorch --deterministic --work-dir path/to/save/outputs

Eval GenAD with 1 GPU

cd /path/to/GenAD
conda activate genad
CUDA_VISIBLE_DEVICES=0 python tools/test.py projects/configs/GenAD/GenAD_config.py /path/to/ckpt.pth --launcher none --eval bbox --tmpdir outputs

Related Projects

Our code is based on VAD and UniAD.

Citation

If you find this project helpful, please consider citing the following paper:

@article{zheng2024genad,
    title={GenAD: Generative End-to-End Autonomous Driving},
    author={Zheng, Wenzhao and Song, Ruiqi and Guo, Xianda and Zhang, Chenming and Chen, Long},
    journal={arXiv preprint arXiv: 2402.11502},
    year={2024}
}