---
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
viewer: false
---


# 📚 CoopScenes Dataset

![CoopScenes Overview Slide](docu/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.