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Datasets:
XieTenghu1
/
TruckV2X

Tasks:
Object Detection
Size:
10K<n<100K
ArXiv:
Tags:
Cooperative Perception
Autonomous Trucking
Dataset
V2X
License:
Dataset card Files
xet
Community
2
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found TruckV2X.py
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 989, in dataset_module_factory
                  raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
              RuntimeError: Dataset scripts are no longer supported, but found TruckV2X.py

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TruckV2X: A Truck-Centered Perception Dataset

Paper

TruckV2X is the first large-scale truck-centered cooperative perception dataset, designed to address unique perception challenges in autonomous trucking (e.g., extensive blind spots, occlusions from large vehicle sizes, and dynamic trailer movements). It features multi-modal sensing (LiDAR and cameras) and supports multi-agent cooperation, including interactions between tractors, trailers, connected and automated vehicles (CAVs), and road-side units (RSUs). This dataset establishes performance benchmarks for heavy-duty vehicle scenarios and accelerates research on multi-agent autonomous trucking systems.

Usage Instructions

Option 1: Load via Hugging Face Hub

You can directly load the dataset using the datasets library for quick experimentation or exploration:

from datasets import load_dataset

dataset = load_dataset("XieTenghu1/TruckV2X", trust_remote_code=True)
# For security reasons, 🤗 Datasets do not allow running dataset loading scripts by default, 
# and you have to pass trust_remote_code=True to load datasets that require running a dataset script.

Note: This method may be more user-friendly and convenient for quick access, but it does not expose the raw file structure, which is required by downstream frameworks like OpenCOOD for direct usage.

Option 2: Clone + Local Extraction (Recommended)

For full flexibility and seamless integration with cooperative perception frameworks (e.g., OpenCOOD), we strongly recommend downloading and using the dataset locally:

# Clone the dataset repository
git lfs install
git clone https://huggingface.co/datasets/XieTenghu1/TruckV2X
cd TruckV2X

# Unzip all splits (each Town is separately zipped)
# Unzip all zip files under train/
for zipfile in train/*.zip; do
  dirname="${zipfile%.zip}"
  unzip "$zipfile" -d "$dirname"
done

# Unzip all zip files under val/
for zipfile in val/*.zip; do
  dirname="${zipfile%.zip}"
  unzip "$zipfile" -d "$dirname"
done

# Unzip all zip files under test/
for zipfile in test/*.zip; do
  dirname="${zipfile%.zip}"
  unzip "$zipfile" -d "$dirname"
done

Dataset Structure

The dataset is organized into training, validation and testing subsets with the following structure:

TruckV2X/
├── train/
│   ├── Town1_1/
│   │   ├── cav/
│   │   │   ├── 000000_camera0.jpg
│   │   │   ├── 000000_camera1.jpg
│   │   │   ├── 000000_camera2.jpg
│   │   │   ├── 000000_camera3.jpg
│   │   │   ├── 000000_lidar0.pcd
│   │   │   ├── 000000.yaml
│   │   │   ├── 000001_xxx
│   │   │   ├── ...
│   │   ├── roadside/
│   │   │   ├── 000000_camera0.jpg
│   │   │   ├── 000000_camera1.jpg
│   │   │   ├── 000000.yaml
│   │   │   ├── 000001_xxx
│   │   │   ├── ...
│   │   ├── tractor/
│   │   │   ├── 000000_camera0.jpg
│   │   │   ├── 000000_camera1.jpg
│   │   │   ├── 000000_camera2.jpg
│   │   │   ├── 000000_camera3.jpg
│   │   │   ├── 000000_camera4.jpg
│   │   │   ├── 000000_lidar0.pcd
│   │   │   ├── 000000_lidar1.pcd
│   │   │   ├── 000000.yaml
│   │   │   ├── 000001_xxx
│   │   │   ├── ...
│   │   ├── trailer/
│   │   │   ├── 000000_camera0.jpg
│   │   │   ├── 000000_camera1.jpg
│   │   │   ├── 000000_camera2.jpg
│   │   │   ├── 000000_camera3.jpg
│   │   │   ├── 000000_camera4.jpg
│   │   │   ├── 000000_lidar0.pcd
│   │   │   ├── 000000_lidar1.pcd
│   │   │   ├── 000000.yaml
│   │   │   ├── 000001_xxx
│   │   │   ├── ...
│   ├── Town1_4/
│   ├── ...
├── val/
│   ├── Town3_0/
│   ├── ...
├── test/
│   ├── Town1_0/
│   ├── ...

Project Page

TBD

License

This dataset is licensed under the MIT License.

Citation

Please cite the paper TruckV2X: A Truck-Centered Perception Dataset if you use this dataset in your work.

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