CoopScenes 0.9.4

Dev-Kit for CoopScenes

CoopScenes

Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving

🚀 Overview

The CoopScenes dataset is a large-scale, multi-scene dataset designed to support research in collective perception, real-time sensor registration, and cooperative intelligent systems for urban mobility. The dataset features synchronized multi-sensor data from both an ego-vehicle and infrastructure sensors, providing researchers with high-quality data for machine learning and sensor fusion applications.

📌 Key Features

✔ 104 minutes of synchronized data at 10 Hz, totaling 62,000 frames
✔ Highly accurate synchronization with a mean deviation of 2.3 ms
✔ Precise point cloud registration between the ego-vehicle and infrastructure sensors
✔ Automated annotation pipelines for object labeling
✔ Open-source anonymization for faces and license plates with BlurScene
✔ Diverse scenarios: public transport hubs, construction sites, and high-speed roads across three cities in Stuttgart, Germany
✔ Total dataset size: 527 GB in .4mse format, accessible via our development kit

📥 Download

The dataset can be accessed via official CoopScenes website and used with our development kit.

📢 INFO: The data will be fully published upon the official publication announcement.

🔧 Installation & Usage

To use the dataset, simply install our provided PyPi package:

    python3 -m pip install CoopScenes

    git clone https://github.com/MarcelVSHNS/CoopScenes.git
    cd CoopScenes
    python -m venv venv # install with apt-get install python3-venv
    source venv/bin/activate
    pip install -r requirements.txt 

Sample Implementation

You can find detailed examples in the Colab notebook.

    import coopscenes as cs
    sample_record = cs.DataRecord("/content/example_record_1.4mse")
    frame = sample_record[0]
    frame.vehicle.cameras.STEREO_LEFT.show()    # PIL Image

📑 Citation

    @misc{vosshans2024aeifdatacollectiondataset,
        author    = {Marcel Vosshans and Alexander Baumann and Matthias Drueppel and Omar Ait-Aider and Ralf Woerner and Youcef Mezouar and Thao Dang and Markus Enzweiler},
        title     = {The AEIF Data Collection: A Dataset for Infrastructure-Supported Perception Research with Focus on Public Transportation},
        url       = {https://arxiv.org/abs/2407.08261},
        year      = {2024},
}

📜 License

This dataset is released under the MIT License.

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Enjoy using CoopScenes! 🚀