README.md

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

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

# Automatum Data: Full Highway 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/)
[![Paper](https://img.shields.io/badge/Paper-IV2021-red)](doc/IV21_Automatumd_Full_Drone_Dataset.pdf)

## Introduction

The **Automatum Data Full Highway Dataset** is a large-scale collection of high-precision vehicle trajectory data extracted from **30 hours of drone video** capturing **12 characteristic highway scenes** along the German A9 Autobahn. With approximately **200,000 tracked vehicles** and over **80,000 km of cumulative trajectory data**, this dataset represents one of the most comprehensive open drone-based highway datasets available.

The processing pipeline incorporates deep learning (Faster R-CNN) for detection and LOESS filtering for stabilization, achieving an exceptionally low **relative velocity error of less than 0.2%**, validated against instrumented reference vehicles.

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

## Dataset at a Glance

| Metric | Value |
|--------|-------|
| **Scenario Type** | Highway (straight segments) |
| **Recordings** | 114 |
| **Locations** | 11 along the A9 Autobahn |
| **Total Duration** | ~30 hours |
| **Total Vehicles Tracked** | ~200,000 |
| **Total Distance** | ~80,000 km |
| **Velocity Error** | < 0.2% (validated with reference vehicles) |
| **Coordinate System** | UTM Zone 32U |
| **FPS** | 29.97 |
| **License** | CC BY-ND 4.0 |

![Highway Scenario](doc/icon_highway.jpg)

## Repository Structure

```
automatum-data-full-highway/
├── README.md                                      # This file
├── doc/                                           # Documentation images, logo, paper
├── example_scripts/                               # Ready-to-use Python analysis scripts
├── Sample_Data/                                   # One recording unpacked for quick preview
│   └── hw-a9-appershofen-001-.../
│       ├── dynamicWorld.json
│       ├── staticWorld.xodr
│       ├── recording.html
│       └── img/
└── automatum_data_full_highway_drone_dataset.zip  # All 114 recordings as archive (~4 GB)
```

> **Quick Preview:** Browse `Sample_Data/` to explore the data structure before downloading the full archive (~4 GB). The sample recording can be loaded directly with the `openautomatumdronedata` Python library.

## KPI Comparison with Established Datasets

| Metric | **Automatum Data** | highD Dataset | NGSIM (US-101 / I-80) |
|--------|-------------------|---------------|------------------------|
| **Total Duration** | **30 hours** | 16.5 hours | ~1.5 hours |
| **Total Vehicles** | **~200,000** | 110,000 | ~thousands |
| **Total Distance** | **~80,000 km** | 45,000 km | limited segments |
| **Source / Perspective** | Drone / Aerial | Drone / Aerial | Fixed Cameras & Drones |
| **Error / Accuracy** | **< 0.2% velocity** | typically < 10 cm | Known clipping issues |
| **Static Description** | **OpenDRIVE XODR** | simple XML/CSV | Basic annotations |
| **Data Format** | **JSON** | CSV | CSV |
| **Object Relationships** | **Built-in (TTC, TTH)** | Must compute | Must compute |
| **OpenSCENARIO** | **Available on request** | No | No |

![ALKS Scenario](doc/icon_alks.jpg)

## Recording Locations

The 114 recordings span 11 locations along the German A9 Autobahn:

| Location | Recordings | Description |
|----------|-----------|-------------|
| Denkendorf | 36 | Major section with high traffic density |
| Stammham | 16 | Mixed traffic scenarios |
| Appershofen | 14 | Varied speed profiles |
| Dunzendorf | 11 | Characteristic highway flow |
| Kinding | 9 | Multi-lane segments |
| Brunn | 9 | Standard highway traffic |
| Hausen | 7 | Diverse driving patterns |
| Untermässing | 6 | Rural highway section |
| Heppberg Park | 3 | Near rest area |
| Apperszell | 2 | Additional coverage |
| Ingolstadt Nord | 1 | Urban highway approach |

## Data Structure

Each recording folder follows the naming convention `hw-a9-{location}-{sequence}-{uuid}` and contains:

```
hw-a9-appershofen-001-uuid/
├── dynamicWorld.json      # Trajectories, velocities, accelerations, bounding boxes
├── staticWorld.xodr       # Road geometry in OpenDRIVE format
├── recording_name.html    # Interactive metadata overview (Bokeh)
└── img/                   # (may contain visualizations)
```

### 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
- Speed limits (typically 100 km/h, unlimited sections)
- Road markings and surface properties

![Static World](doc/static_world_fig_02.png)
![Static World Detail](doc/static_world_fig_04.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("hw-a9-appershofen-001-uuid")
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=5.0s
objects = dynWorld.get_list_of_dynamic_objects_for_specific_time(5.0)
for obj in objects[:5]:
    speed_kmh = ((obj.vx_vec[0]**2 + obj.vy_vec[0]**2)**0.5) * 3.6
    print(f"  {obj.UUID} ({obj.type}) — {speed_kmh:.1f} km/h")
```

### 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 (~200 MB)
local_path = snapshot_download(
    repo_id="AutomatumData/automatum-data-full-highway",
    repo_type="dataset",
    allow_patterns=["Sample_Data/**"]
)

# Option 2: Download the full archive (~4 GB)
archive = hf_hub_download(
    repo_id="AutomatumData/automatum-data-full-highway",
    filename="automatum_data_full_highway_drone_dataset.zip",
    repo_type="dataset"
)
# Extract
with zipfile.ZipFile(archive, 'r') as z:
    z.extractall("automatum_data_full_highway")

# Load with openautomatumdronedata
from openautomatumdronedata.dataset import droneDataset
dataset = droneDataset("automatum_data_full_highway/hw-a9-appershofen-001-d8087340-8287-46b6-9612-869b09e68448")
print(f"Vehicles: {len(dataset.dynWorld)}")
```

### Batch Processing All Recordings

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

base_path = "path/to/automatum_data_full_highway_drone_dataset"

stats = []
for folder in sorted(os.listdir(base_path)):
    full_path = os.path.join(base_path, folder)
    if not os.path.isdir(full_path) or not folder.startswith("hw-"):
        continue
    
    dataset = droneDataset(full_path)
    dw = dataset.dynWorld
    
    stats.append({
        "recording": folder,
        "vehicles": len(dw),
        "duration_s": dw.maxTime,
        "frames": dw.frame_count,
    })
    print(f"{folder}: {len(dw)} vehicles, {dw.maxTime:.0f}s")

# Save summary
with open("dataset_summary.json", "w") as f:
    json.dump(stats, f, indent=2)
```

## 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

## Research Paper

The methodology and validation of this dataset are described in our peer-reviewed publication:

> **AUTOMATUM DATA: Drone-based highway dataset for development and validation of automated driving software for research and commercial applications**
> Paul Spannaus, Peter Zechel, Kilian Lenz
> *IEEE Intelligent Vehicles Symposium (IV), 2021*

The paper is included in this repository: [`doc/IV21_Automatumd_Full_Drone_Dataset.pdf`](doc/IV21_Automatumd_Full_Drone_Dataset.pdf)

Key findings from the paper:

- Processing pipeline validated with instrumented reference vehicles
- Relative velocity error < 0.2%
- Deep learning detection (Faster R-CNN) combined with LOESS filtering
- High-precision UTM world coordinate mapping
- Standardized OpenDRIVE export for seamless integration with simulation tools

## Research Use & Extended Data Pool

**These publicly available datasets are intended exclusively for research purposes.**

This dataset, while comprehensive, is still an excerpt from the full **Automatum Data Pool** containing over **1,000 hours of processed drone video** across highways, intersections, roundabouts, and urban scenarios. 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)