This is the official implementation of CVPR2023 Highlight paper. "V2V4Real: A large-scale real-world dataset for Vehicle-to-Vehicle Cooperative Perception". Runsheng Xu, Xin Xia, Jinlong Li, Hanzhao Li, Shuo Zhang, Zhengzhong Tu, Zonglin Meng, Hao Xiang, Xiaoyu Dong, Rui Song, Hongkai Yu, Bolei Zhou, Jiaqi Ma

Supported by the UCLA Mobility Lab.

• Codebase Features
• Data Download
• Changelog
• Devkit Setup
• Quick Start
• Benchmark
• Citation
• Acknowledgment

• Support both simulation and real-world cooperative perception dataset
  • V2V4Real
  • OPV2V
• Multiple Tasks supported
  • 3D object detection
  • Cooperative tracking
  • Sim2Real
• SOTA model supported
  • Attentive Fusion [ICRA2022]
  • Cooper [ICDCS]
  • F-Cooper [SEC2019]
  • V2VNet [ECCV2020]
  • V2X-ViT [ECCV2022]
  • CoBEVT [CoRL2022]

Please check our website to download the data (OPV2V format).

After downloading the data, please put the data in the following structure:

├── v2v4real
│   ├── train
|      |── testoutput_CAV_data_2022-03-15-09-54-40_1
│   ├── validate
│   ├── test

• Oct. 22, 2023: Kitti format data is released google drive
• Mar. 21, 2023: Sim2Real related codebase and pre-trained models are released
• Apr. 08, 2023: Dateset and pre-trained models are released
• Mar. 23, 2023: The codebase for 3D object detection is released
• Mar. 19, 2023: The website is ready
• Mar. 14, 2023: Tha paper is released

V2V4Real's codebase is build upon OpenCOOD. Compared to OpenCOOD, this codebase supports both the simulation and real-world data and more perception tasks. Furthermore, this repo provides augmentations that OpenCOOD does not support. We highly recommend you to use this codebase to train your model on V2V4Real dataset

To set up the codebase environment, do the following steps:

conda create -n v2v4real python=3.7
conda activate v2v4real

Take pytorch 1.12.0 as an example:

conda install pytorch==1.12.0 torchvision==0.13.0 cudatoolkit=11.3 -c pytorch -c conda-forge

pip install spconv-cu113

pip install -r requirements.txt
python setup.py develop

python opencood/utils/setup.py build_ext --inplace

To quickly visualize the LiDAR stream in the OPV2V dataset, first modify the validate_dir in your opencood/hypes_yaml/visualization.yaml to the opv2v data path on your local machine, e.g. opv2v/validate, and the run the following commond:

cd ~/OpenCOOD
python opencood/visualization/vis_data_sequence.py [--color_mode ${COLOR_RENDERING_MODE} --isSim]

Arguments Explanation:

• color_mode : str type, indicating the lidar color rendering mode. You can choose from 'v2vreal', 'constant', 'intensity' or 'z-value'.
• isSim : bool type, if you are visualizing the simulation data, then claim this argument.

OpenCOOD uses yaml file to configure all the parameters for training. To train your own model from scratch or a continued checkpoint, run the following commonds:

python opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir  ${CHECKPOINT_FOLDER} --half]

Arguments Explanation:

• hypes_yaml: the path of the training configuration file, e.g. opencood/hypes_yaml/point_pillar_fax.yaml, meaning you want to train CoBEVT with pointpillar backbone. See Tutorial 1: Config System to learn more about the rules of the yaml files.
• model_dir (optional) : the path of the checkpoints. This is used to fine-tune the trained models. When the model_dir is given, the trainer will discard the hypes_yaml and load the config.yaml in the checkpoint folder.
• half (optional): If set, the model will be trained with half precision. It cannot be set with multi-gpu training togetger.

To train on multiple gpus, run the following command:

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4  --use_env opencood/tools/train.py --hypes_yaml ${CONFIG_FILE} [--model_dir  ${CHECKPOINT_FOLDER}]

We provide train_da.py to train the sim2real models shown in the paper. The models will take the simulation data and v2v4real data without gt labels as input, and compute the domain adaptation loss. To train the sim2real model, run the following command:

python opencood/tools/train_da.py --hypes_yaml hypes_yaml/domain_adaptions/xxx.yaml [--model_dir  ${CHECKPOINT_FOLDER} --half

Before you run the following command, first make sure the validation_dir in config.yaml under your checkpoint folder refers to the testing dataset path, e.g. v2v4real/test.

python opencood/tools/inference.py --model_dir ${CHECKPOINT_FOLDER} --fusion_method ${FUSION_STRATEGY} [--show_vis] [--show_sequence]

Arguments Explanation:

• model_dir: the path to your saved model.
• fusion_method: indicate the fusion strategy, currently support 'nofusion', 'early', 'late', and 'intermediate'.
• show_vis: whether to visualize the detection overlay with point cloud.
• show_sequence : the detection results will visualized in a video stream. It can NOT be set with show_vis at the same time.

The evaluation results will be dumped in the model directory.

Important notes for testing:

1. Remember to change the validation_dir in config.yaml under your checkpoint folder to the testing dataset path, e.g. v2v4real/test.
2. To test under async mode, you need to set the async_mode in config.yaml to True and set the async_overhead to the desired delay time (default 100ms).
3. The testing script for cooperative 3D object detection and sim2real is the same

[1]: Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object de- tection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339–3348, 2018.

@inproceedings{xu2023v2v4real,
  title={V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception},
  author={Xu, Runsheng and Xia, Xin and Li, Jinlong and Li, Hanzhao and Zhang, Shuo and Tu, Zhengzhong and Meng, Zonglin and Xiang, Hao and Dong, Xiaoyu and Song, Rui and Yu, Hongkai and Zhou, Bolei and Ma, Jiaqi},
  booktitle={The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
  year={2023}
}

This dataset belongs to the OpenCDA ecosystem family. The codebase is build upon OpenCOOD, which is the first Open Cooperative Detection framework for autonomous driving.