Datasets:

AutomatumData
/

automatum-data-crossing

---
language:
- en
- de
license: cc-by-nd-4.0
tags:
- autonomous-driving
- traffic-analysis
- trajectory-prediction
- drone-data
- automatum
- open-drive
- json
- t-crossing
- intersection
- openscenario
pretty_name: "Automatum Data: T-Crossing Drone Dataset"
task_categories:
- time-series-forecasting
- object-detection
size_categories:
- 1K<n<10K
---

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

# Automatum Data: T-Crossing 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 T-Crossing Dataset** contains high-precision movement data of traffic participants (cars, trucks, vans) extracted from drone recordings at T-shaped intersections in Bavaria, Germany. The data is captured from a bird's eye view and provides complete trajectories with velocities, accelerations, lane assignments, and object relationships.

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** | T-Crossing / Intersection |
| **Recordings** | 2 |
| **Total Duration** | ~30 minutes (0.49 hours) |
| **Total Distance** | 108.8 km |
| **Total Vehicles Tracked** | 683 |
| **Vehicle Types** | 623 Cars, 47 Trucks, 13 Vans |
| **Max Trajectory Length** | 160.3 m |
| **Coordinate System** | UTM Zone 32U |
| **FPS** | 29.97 |
| **License** | CC BY-ND 4.0 |

![T-Crossing Scenario](doc/icon_crossing.jpg)

## Repository Structure

```
automatum-data-crossing/
├── 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
│   └── T-Crossing--GaimersheimStadtweg_e2e6-.../
│       ├── dynamicWorld.json
│       ├── staticWorld.xodr
│       ├── recording.html
│       └── img/
└── automatum_data_crossing.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. T-Crossing Gaimersheim Stadtweg

| | |
|---|---|
| ![Map](doc/map_gaimersheim.jpg) | ![Trajectories](doc/trajectories_gaimersheim.jpg) |

| KPI | Value |
|-----|-------|
| Trajectories | 299 |
| Duration | 650.7 s (~10.8 min) |
| Traffic Flow | 1,654.3 veh/h |
| Traffic Density | 40.2 veh/km |
| Avg. Speed | 41.2 km/h |
| Max. Speed | 109.4 km/h |
| Max. Acceleration | 4.7 m/s² |
| Location | 48.7882°N, 11.3855°E |

### 2. T-Crossing St2214 Dünzlau Umgehung

| | |
|---|---|
| ![Map](doc/map_duenzlau.jpg) | ![Trajectories](doc/trajectories_duenzlau.jpg) |

| KPI | Value |
|-----|-------|
| Trajectories | 384 |
| Duration | 1,125.6 s (~18.8 min) |
| Traffic Flow | 1,228.1 veh/h |
| Traffic Density | 22.1 veh/km |
| Avg. Speed | 55.6 km/h |
| Max. Speed | 110.4 km/h |
| Max. Acceleration | 5.8 m/s² |
| Location | 48.7762°N, 11.3196°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
- 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("T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2")
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-crossing",
    repo_type="dataset",
    allow_patterns=["Sample_Data/**"]
)

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

# Load with openautomatumdronedata
from openautomatumdronedata.dataset import droneDataset
dataset = droneDataset("automatum_data_crossing/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2")
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
- **`04_export_objects.py`** — Export per-vehicle JSON files with surrounding object data

## 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)