Datasets:

AutomatumData
/

automatum-data-roundabout

	---
	language:
	- en
	- de
	license: cc-by-nd-4.0
	tags:
	- autonomous-driving
	- traffic-analysis
	- trajectory-prediction
	- drone-data
	- automatum
	- open-drive
	- json
	- roundabout
	- intersection
	- openscenario
	pretty_name: "Automatum Data: Roundabout Drone Dataset"
	task_categories:
	- time-series-forecasting
	- object-detection
	size_categories:
	- 100<n<1K
	---

	![Automatum Data Logo](doc/automatum_logo.png)

	# Automatum Data: Roundabout Drone Dataset

	[![Website](https://img.shields.io/badge/Website-automatum--data.com-blue)](https://automatum-data.com)
	[![Documentation](https://img.shields.io/badge/Docs-ReadTheDocs-green)](https://openautomatumdronedata.readthedocs.io)
	[![PyPI](https://img.shields.io/badge/PyPI-openautomatumdronedata-orange)](https://pypi.org/project/openautomatumdronedata/)
	[![License](https://img.shields.io/badge/License-CC%20BY--ND%204.0-lightgrey)](https://creativecommons.org/licenses/by-nd/4.0/)

	## Introduction

	The Automatum Data Roundabout Dataset contains high-precision movement data of traffic participants (cars, trucks, vans) extracted from drone recordings at roundabout intersections in Bavaria, Germany. Captured from a bird's eye view, the dataset provides complete trajectories with velocities, accelerations, lane assignments, and object relationships — uniquely suited for roundabout behavior modeling.

	This dataset directly competes with established benchmarks such as highD and NGSIM — offering superior data quality (JSON instead of CSV), standardized road geometry (OpenDRIVE XODR), and precise UTM world coordinate mapping.

	![Illustration of Drone Data Extraction](doc/illustration.jpg)

	## Dataset at a Glance

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Scenario Type \| Roundabout \|
	\| Recordings \| 2 \|
	\| Total Duration \| ~7 minutes (0.12 hours) \|
	\| Total Distance \| 26.0 km \|
	\| Total Vehicles Tracked \| 212 \|
	\| Vehicle Types \| 207 Cars, 3 Trucks, 2 Vans \|
	\| Max Trajectory Length \| 125.5 m \|
	\| Coordinate System \| UTM Zone 32U \|
	\| FPS \| 29.97 \|
	\| License \| CC BY-ND 4.0 \|

	![Roundabout Scenario](doc/icon_roundabout.jpg)

	## Repository Structure

	```
	automatum-data-roundabout/
	├── README.md # This file
	├── doc/ # Documentation images, logo, technical diagrams
	├── example_scripts/ # Ready-to-use Python analysis scripts
	├── Sample_Data/ # One recording unpacked for quick preview
	│ └── Roundabout-St2231-IngolstadtVOC_e63d-.../
	│ ├── dynamicWorld.json
	│ ├── staticWorld.xodr
	│ ├── recording.html
	│ └── img/
	└── automatum_data_roundabout.zip # All recordings as archive
	```

	> Quick Preview: Browse `Sample_Data/` to explore the data structure before downloading the full archive. The sample recording can be loaded directly with the `openautomatumdronedata` Python library.

	## Recording Overview

	### 1. Roundabout St2231 Ingolstadt VOC

	\| \| \|
	\|---\|---\|
	\| ![Map](doc/map_ingolstadt.jpg) \| ![Trajectories](doc/trajectories_ingolstadt.jpg) \|

	\| KPI \| Value \|
	\|-----\|-------\|
	\| Trajectories \| 148 \|
	\| Duration \| 288.6 s (~4.8 min) \|
	\| Traffic Flow \| 1,846.0 veh/h \|
	\| Traffic Density \| 139.9 veh/km \|
	\| Avg. Speed \| 13.2 km/h \|
	\| Max. Speed \| 54.6 km/h \|
	\| Max. Acceleration \| 3.9 m/s² \|
	\| Location \| 48.7841°N, 11.4821°E \|

	### 2. Roundabout Köschinger Tor, Kösching

	\| \| \|
	\|---\|---\|
	\| ![Map](doc/map_koesching.jpg) \| ![Trajectories](doc/trajectories_koesching.jpg) \|

	\| KPI \| Value \|
	\|-----\|-------\|
	\| Trajectories \| 64 \|
	\| Duration \| 133.5 s (~2.2 min) \|
	\| Traffic Flow \| 1,726.3 veh/h \|
	\| Traffic Density \| 65.1 veh/km \|
	\| Avg. Speed \| 26.5 km/h \|
	\| Max. Speed \| 55.8 km/h \|
	\| Max. Acceleration \| 4.2 m/s² \|
	\| Location \| 48.8087°N, 11.4804°E \|

	## Data Structure

	Each recording folder contains:

	```
	recording_folder/
	├── dynamicWorld.json # Trajectories, velocities, accelerations, bounding boxes
	├── staticWorld.xodr # Road geometry in OpenDRIVE format
	├── recording_name.html # Interactive metadata overview (Bokeh)
	└── img/
	├── kpis.json # Key performance indicators
	├── *_map.jpg # Aerial map view
	├── *_trajectories.jpg # Trajectory visualization
	└── *_centerImg_thumb.jpg # Center frame thumbnail
	```

	### dynamicWorld.json

	The core data file contains for each tracked vehicle:

	- Position vectors: `x_vec`, `y_vec` — UTM coordinates over time
	- Velocity vectors: `vx_vec`, `vy_vec` — in m/s
	- Acceleration vectors: `ax_vec`, `ay_vec` — in m/s²
	- Jerk vectors: `jerk_x_vec`, `jerk_y_vec`
	- Heading: `psi_vec` — orientation angle
	- Lane assignment: `lane_id_vec`, `road_id_vec` — linked to XODR
	- Object dimensions: `length`, `width`
	- Object relationships: `object_relation_dict_list` — front/behind/left/right neighbors
	- Safety metrics: `ttc_dict_vec` (Time-to-Collision), `tth_dict_vec` (Time-to-Headway)
	- Lane distances: `distance_left_lane_marking`, `distance_right_lane_marking`

	![Vehicle Dynamics](doc/VehicleDynamics.png)

	### staticWorld.xodr

	OpenDRIVE 1.6 format file defining:

	- Road network topology and geometry
	- Lane definitions with widths and types
	- Junction configurations (roundabout-specific)
	- Speed limits and road markings

	![Static World](doc/static_world_fig_02.png)

	### Key Metrics Explained

	![Time-to-Collision](doc/ttc.png)
	![Lane Distance](doc/lane_distance.png)
	![Point-to-Lane Assignment](doc/point_to_lane_assignement_Sans.png)

	## Quick Start

	### Installation

	```bash
	pip install openautomatumdronedata
	```

	### Load and Explore

	```python
	from openautomatumdronedata.dataset import droneDataset
	import os

	# Point to one recording folder
	path = os.path.abspath("Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d")
	dataset = droneDataset(path)

	# Access dynamic world
	dynWorld = dataset.dynWorld

	print(f"UUID: {dynWorld.UUID}")
	print(f"Duration: {dynWorld.maxTime:.1f} seconds")
	print(f"Frames: {dynWorld.frame_count}")
	print(f"Vehicles: {len(dynWorld)}")

	# Get all vehicles visible at t=1.0s
	objects = dynWorld.get_list_of_dynamic_objects_for_specific_time(1.0)
	for obj in objects[:5]:
	print(f" {obj.UUID} ({obj.type}) — x={obj.x_vec[0]:.1f}, y={obj.y_vec[0]:.1f}")
	```

	### Using with Hugging Face

	```python
	from huggingface_hub import snapshot_download, hf_hub_download
	import zipfile, os

	# Option 1: Download only the sample for a quick look
	local_path = snapshot_download(
	repo_id="AutomatumData/automatum-data-roundabout",
	repo_type="dataset",
	allow_patterns=["Sample_Data/**"]
	)

	# Option 2: Download the full archive
	archive = hf_hub_download(
	repo_id="AutomatumData/automatum-data-roundabout",
	filename="automatum_data_roundabout.zip",
	repo_type="dataset"
	)
	# Extract
	with zipfile.ZipFile(archive, 'r') as z:
	z.extractall("automatum_data_roundabout")

	# Load with openautomatumdronedata
	from openautomatumdronedata.dataset import droneDataset
	dataset = droneDataset("automatum_data_roundabout/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d")
	print(f"Vehicles: {len(dataset.dynWorld)}")
	```

	## Example Scripts

	See the `example_scripts/` folder for ready-to-use analysis scripts:

	- `01_lane_changes.py` — Analyze lane change behavior across all vehicles
	- `02_heatmap_density.py` — Generate traffic density heatmaps
	- `03_high_acceleration.py` — Detect high-acceleration events

	## Comparison with Established Datasets

	\| Feature \| Automatum Data \| highD \| NGSIM \|
	\|---------\|---------------\|-------\|-------\|
	\| Data Format \| JSON + OpenDRIVE XODR \| CSV + XML \| CSV \|
	\| Road Geometry \| OpenDRIVE 1.6 standard \| Simple annotations \| Basic annotations \|
	\| Coordinate System \| UTM world coordinates \| Local coordinates \| Local coordinates \|
	\| Object Relationships \| Built-in (TTC, TTH, distances) \| Must compute \| Must compute \|
	\| Velocity Error \| < 0.2% (validated) \| < 10 cm positional \| Known issues \|
	\| Python Library \| `openautomatumdronedata` \| Custom scripts \| Custom scripts \|
	\| OpenSCENARIO \| Available on request \| No \| No \|

	## Research Use & Extended Data Pool

	These publicly available datasets are intended exclusively for research purposes.

	This dataset is a small excerpt from the comprehensive Automatum Data Pool containing over 1,000 hours of processed drone video. For commercial use or access to further datasets, including OpenSCENARIO exports, please contact us via our website:

	[automatum-data.com](https://automatum-data.com)

	## Citation

	If you use this dataset in your research, please cite:

	```bibtex
	@inproceedings{spannaus2021automatum,
	title={AUTOMATUM DATA: Drone-based highway dataset for development and validation of automated driving software},
	author={Spannaus, Paul and Zechel, Peter and Lenz, Kilian},
	booktitle={IEEE Intelligent Vehicles Symposium (IV)},
	year={2021}
	}
	```

	## License

	This dataset is licensed under [Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)](https://creativecommons.org/licenses/by-nd/4.0/).

	## Contact

	- Website: [automatum-data.com](https://automatum-data.com)
	- Email: info@automatum-data.com
	- HuggingFace: [AutomatumData](https://huggingface.co/AutomatumData)
	- Documentation: [openautomatumdronedata.readthedocs.io](https://openautomatumdronedata.readthedocs.io)