--- 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 **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)