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readme.md

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+---
+license: mit
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- perception
+- cooperative
+- collective
+- autonomous_driving
+pretty_name: >-
+  CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing
+  Collective Perception in Autonomous Driving
+---
+
+
+# 📚 CoopScenes Dataset
+
+![CoopScenes Overview Slide](Coop-Scenes.png)
+
+## 🌟 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](https://coopscenes.github.io/)
+
+---
+
+## 🛠️ 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**](https://pypi.org/project/BlurScene/)
+
+---
+
+## ✅ 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 |
+
+---
+
+## 📦 Installation & Usage
+
+Install the CoopScenes Python package:
+
+```bash
+pip install coopscenes
+```
+
+Then load and explore the dataset using the included developer tools:
+
+```python
+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](https://pypi.org/project/coopscenes/).
+
+---
+
+## 🚀 Google Colab (Quickstart)
+
+Get started with the data using our ready-to-run [**Colab notebook**](https://coopscenes.github.io/#colab). It demonstrates:
+
+- Reading `.4mse` files
+- Visualizing sensor data
+- Performing simple analysis tasks
+
+---
+
+## 📄 Citation
+
+Please cite the following if you use CoopScenes in your work (IEEE IV '25 publish is following):
+
+```bibtex
+@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},
+}
+```
+
+---
+
+## 🔐 License
+
+The dataset is released under the **MIT License**. Refer to the LICENSE file for details.
+