AutomatumData/automatum-data-highway-with-ramps

Automatum Data: Highway with Ramps Drone Dataset

Introduction

The Automatum Data Highway with Ramps Dataset contains high-precision movement data of traffic participants (cars, trucks, vans) extracted from drone recordings on German Autobahn segments with on-ramps and off-ramps. Captured from a bird's eye view, the dataset provides complete trajectories with velocities, accelerations, lane assignments, and object relationships — perfectly suited for merging behavior analysis, ALKS (Automated Lane Keeping System) validation, and highway scenario generation.

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.

Dataset at a Glance

Metric	Value
Scenario Type	Highway with On/Off-Ramps
Recordings	4
Locations	A9 Kinding, A8 Brunnthal, A99 Feldkirchen, A9 Allersberg
Total Duration	~58 minutes (0.96 hours)
Total Distance	4,555.3 km
Total Vehicles Tracked	7,178
Vehicle Types	6,415 Cars, 466 Trucks, 229 Vans
Max Trajectory Length	639.0 m
Coordinate System	UTM Zone 32U
FPS	29.97
License	CC BY-ND 4.0

Repository Structure

automatum-data-highway-with-ramps/
├── 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
│   └── Highway-A9-Kinding_45c1-.../
│       ├── dynamicWorld.json
│       ├── staticWorld.xodr
│       ├── recording.html
│       └── img/
└── automatum_data_highway_with_ramps.zip  # All recordings as archive

Quick Preview: Browse Sample_Data/ to explore the data structure before downloading the full archive (~1.5 GB). The sample recording can be loaded directly with the openautomatumdronedata Python library.

Recording Overview

1. Highway A9 — Kinding

KPI	Value
Trajectories	1,131
Duration	582.0 s (~9.7 min)
Traffic Flow	6,995.7 veh/h
Traffic Density	56.7 veh/km
Avg. Trajectory Length	623.6 m
Avg. Speed	123.5 km/h
Max. Speed	201.1 km/h
Max. Acceleration	6.3 m/s²
Location	48.9970°N, 11.3763°E

2. Highway A8 — Brunnthal Süd

KPI	Value
Trajectories	2,250
Duration	874.2 s (~14.6 min)
Traffic Flow	9,265.9 veh/h
Traffic Density	89.9 veh/km
Avg. Trajectory Length	639.0 m
Avg. Speed	103.1 km/h
Max. Speed	187.0 km/h
Max. Acceleration	4.8 m/s²
Location	48.0072°N, 11.6713°E

3. Highway A99 — Feldkirchen Nord

KPI	Value
Trajectories	1,801
Duration	1,136.9 s (~19.0 min)
Traffic Flow	5,703.0 veh/h
Traffic Density	53.4 veh/km
Avg. Trajectory Length	636.7 m
Avg. Speed	106.9 km/h
Max. Speed	221.9 km/h
Max. Acceleration	5.2 m/s²
Location	48.1487°N, 11.7569°E

4. Highway A9 — Allersberg

KPI	Value
Trajectories	1,996
Duration	873.0 s (~14.6 min)
Traffic Flow	8,230.6 veh/h
Traffic Density	77.8 veh/km
Avg. Trajectory Length	634.0 m
Avg. Speed	105.7 km/h
Max. Speed	195.7 km/h
Max. Acceleration	7.4 m/s²
Location	49.2529°N, 11.2169°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

staticWorld.xodr

OpenDRIVE 1.6 format file defining:

• Road network topology and geometry (incl. ramp connections)
• Lane definitions with widths and types
• Junction and ramp configurations
• Speed limits and road markings

Key Metrics Explained

Quick Start

Installation

pip install openautomatumdronedata

Load and Explore

from openautomatumdronedata.dataset import droneDataset
import os

# Point to one recording folder
path = os.path.abspath("Highway-A9-Allersberg_9b82-9b822c8f-b3dc-4c5c-824d-2354203d0e7b")
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}) — v={((obj.vx_vec[0]**2+obj.vy_vec[0]**2)**0.5)*3.6:.1f} km/h")

Using with Hugging Face

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-highway-with-ramps",
    repo_type="dataset",
    allow_patterns=["Sample_Data/**"]
)

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

# Load with openautomatumdronedata
from openautomatumdronedata.dataset import droneDataset
dataset = droneDataset("automatum_data_highway_with_ramps/Highway-A9-Kinding_45c1-45c1201e-ada4-46da-b885-c7cfa4b0cf33")
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
Ramp Scenarios	Yes (on/off-ramps)	No ramps	Limited
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

Citation

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

@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).

Contact

• Website: automatum-data.com
• Email: info@automatum-data.com
• HuggingFace: AutomatumData
• Documentation: openautomatumdronedata.readthedocs.io