📚 CoopScenes Dataset

🌟 Overview

CoopScenes is a comprehensive multi-scene dataset designed for research in collective perception, sensor registration, and cooperative systems in urban environments. It features synchronized data from an ego vehicle and infrastructure-mounted sensors across real-world scenarios, including public transport stops, construction sites, and expressways.

• Duration: ~104 minutes at 10 Hz → ~62,000 frames (~527 GB in .4mse format)
• Synchronization: Sub-frame alignment with ~2.3 ms mean offset
• Scenarios: Collected across multiple cities in the Stuttgart metropolitan area

🔜 More information: coopscenes.github.io

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🛠️ Sensor Setup & Annotations

The dataset features time-synchronized and spatially calibrated sensors on both the ego vehicle and roadside infrastructure (towers), including:

• LiDAR (Ouster OS2, Blickfeld Qb2)
• Multi-camera systems
• GNSS and IMU
• Object annotations (automatically generated)
• Privacy-preserving anonymization using BlurScene

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✅ Key Features

Feature	Description
62,000 Frames at 10 Hz	~104 minutes of data
High-precision synchronization	Mean offset ~2.3 ms
Vehicle-to-infrastructure setup	Multi-agent cooperative perception
Diverse scenarios	Public transport, construction, highways
Automatic annotations & anonymization	Faces and license plates blurred with BlurScene

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📦 Installation & Usage

Install the CoopScenes Python package:

pip install coopscenes

Then load and explore the dataset using the included developer tools:

from coopscenes import DataRecord

# open a specific .4mse-file
record = DataRecord("/content/example_record_1.4mse")
# use first frame from record
frame = record[0]

frame.vehicle.cameras.STEREO_LEFT.show()
print(frame.tower.lidars.UPPER_PLATFORM.points.shape)  # example access

Additional tooling, documentation, and format specs can be found in the developer toolkit.

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🚀 Google Colab (Quickstart)

Get started with the data using our ready-to-run Colab notebook. It demonstrates:

• Reading .4mse files
• Visualizing sensor data
• Performing simple analysis tasks

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📄 Citation

Please cite the following if you use CoopScenes in your work (IEEE IV '25 publish is following):

@misc{vosshans2025aeifdatacollectiondataset,
  author    = {Marcel Vosshans and Alexander Baumann and Matthias Drueppel and Omar Ait-Aider and Youcef Mezouar and Thao Dang and Markus Enzweiler},
  title     = {CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving},
  url       = {https://arxiv.org/abs/2407.08261},
  year      = {2025},
}

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🔐 License

The dataset is released under the MIT License. Refer to the LICENSE file for details.