Upload automatum_data_full_highway_drone_dataset — Sample_Data, docs, examples, archive

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+Sample_Data/hw-a9-appershofen-001-d8087340-8287-46b6-9612-869b09e68448/dynamicWorld.json filter=lfs diff=lfs merge=lfs -text
+doc/IV21_Automatumd_Full_Drone_Dataset.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,309 @@
+---
+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)

Sample_Data/hw-a9-appershofen-001-d8087340-8287-46b6-9612-869b09e68448/dynamicWorld.json

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Sample_Data/hw-a9-appershofen-001-d8087340-8287-46b6-9612-869b09e68448/staticWorld.xodr

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+<OpenDRIVE xmlns="http://www.opendrive.org">
+    <header revMajor="1" revMinor="4" date="2021-15-06T07:59:06">
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+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </left>
+                <center>
+                    <lane id="0" type="driving" />
+                </center>
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+                        <roadMark sOffset="1e-07" type="solid" color="white" width="0.12" height="0" />
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+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
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+                        <roadMark sOffset="1e-07" type="broken" color="white" width="0.12" height="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
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+                        <roadMark sOffset="1e-07" type="broken" color="white" width="0.12" height="0" />
+                        <material sOffset="0" friction="1" />
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+                        <material sOffset="0" friction="1" />
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+                        <material sOffset="0" friction="1" />
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+        <objects>
+            <object type="IPGRoad Traffic barrier" name="object 19" id="1" s="1e-07" t="0.3" zOffset="0" validLength="270.3939998" orientation="none" hdg="0" pitch="0" roll="0" length="270.3939998" width="0.155" height="0.75">
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automatum_data_full_highway_drone_dataset.zip

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doc/IV21_Automatumd_Full_Drone_Dataset.pdf

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example_scripts/01_lane_changes.py

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+"""
+Lane Change Analysis — Automatum Data Full Highway Dataset
+Analyzes lane change behavior across all vehicles in a recording.
+
+Usage:
+    python 01_lane_changes.py <path_to_recording_folder>
+
+Example:
+    python 01_lane_changes.py ../hw-a9-denkendorf-001-d8087340-8287-46b6-9612-869b09e68448
+"""
+import sys
+import os
+from openautomatumdronedata.dataset import droneDataset
+
+
+def analyze_lane_changes(dataset_path):
+    print(f"Loading dataset from: {dataset_path}")
+    dataset = droneDataset(dataset_path)
+    dynWorld = dataset.dynWorld
+
+    print(f"Vehicles found: {len(dynWorld)}")
+
+    lane_change_counts = []
+
+    for dynObj in dynWorld.dynamicObjects.values():
+        lane_ids = dynObj.lane_id_vec
+        if len(lane_ids) == 0:
+            continue
+
+        changes = 0
+        current_lane = lane_ids[0]
+        for lane in lane_ids[1:]:
+            if lane != current_lane:
+                changes += 1
+                current_lane = lane
+
+        lane_change_counts.append({
+            "uuid": dynObj.UUID,
+            "type": dynObj.type,
+            "changes": changes,
+        })
+
+    sorted_by_changes = sorted(lane_change_counts, key=lambda x: x["changes"], reverse=True)
+
+    print("\n--- Top 10 vehicles with most lane changes ---")
+    for idx, item in enumerate(sorted_by_changes[:10]):
+        print(f"{idx+1}. Vehicle {item['uuid']} ({item['type']}): {item['changes']} lane changes")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python 01_lane_changes.py <path_to_recording_folder>")
+    else:
+        analyze_lane_changes(sys.argv[1])

example_scripts/02_heatmap_density.py

@@ -0,0 +1,58 @@
+"""
+Traffic Density Heatmap — Automatum Data Full Highway Dataset
+Generates a 2D heatmap of all vehicle positions over time.
+
+Usage:
+    python 02_heatmap_density.py <path_to_recording_folder>
+
+Example:
+    python 02_heatmap_density.py ../hw-a9-denkendorf-001-d8087340-8287-46b6-9612-869b09e68448
+
+Output:
+    traffic_heatmap.png in the current directory
+"""
+import sys
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from openautomatumdronedata.dataset import droneDataset
+
+
+def generate_density_heatmap(dataset_path, output_filename="traffic_heatmap.png"):
+    print(f"Loading dataset from: {dataset_path}")
+    dataset = droneDataset(dataset_path)
+    dynWorld = dataset.dynWorld
+
+    print("Extracting position data...")
+    all_x, all_y = [], []
+
+    for dynObj in dynWorld.dynamicObjects.values():
+        x_valid = [x for x in dynObj.x_vec if not np.isnan(x)]
+        y_valid = [y for y in dynObj.y_vec if not np.isnan(y)]
+        all_x.extend(x_valid)
+        all_y.extend(y_valid)
+
+    if not all_x:
+        print("No position data found!")
+        return
+
+    print(f"Extracted {len(all_x)} data points. Creating heatmap...")
+
+    plt.figure(figsize=(16, 6))
+    plt.style.use("dark_background")
+    plt.hist2d(all_x, all_y, bins=(300, 100), cmap="inferno", cmin=1)
+    plt.colorbar(label="Traffic density (data points)")
+    plt.title("Highway Traffic Density Heatmap (Top-View)")
+    plt.xlabel("X-Position [m]")
+    plt.ylabel("Y-Position [m]")
+    plt.gca().set_aspect("equal", adjustable="box")
+    plt.tight_layout()
+    plt.savefig(output_filename, dpi=300)
+    print(f"Heatmap saved: {os.path.abspath(output_filename)}")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python 02_heatmap_density.py <path_to_recording_folder>")
+    else:
+        generate_density_heatmap(sys.argv[1])

example_scripts/03_high_acceleration.py

@@ -0,0 +1,61 @@
+"""
+High Acceleration Detection — Automatum Data Full Highway Dataset
+Detects vehicles exceeding a given acceleration threshold.
+
+Usage:
+    python 03_high_acceleration.py <path_to_recording_folder> [threshold_m_s2]
+
+Example:
+    python 03_high_acceleration.py ../hw-a9-denkendorf-001-d8087340-8287-46b6-9612-869b09e68448 3.0
+"""
+import sys
+import os
+import numpy as np
+from openautomatumdronedata.dataset import droneDataset
+
+
+def detect_high_accelerations(dataset_path, acc_threshold=3.0):
+    print(f"Loading dataset from: {dataset_path}")
+    dataset = droneDataset(dataset_path)
+    dynWorld = dataset.dynWorld
+
+    print(f"Searching for accelerations > {acc_threshold} m/s^2...")
+
+    high_accel_vehicles = []
+
+    for dynObj in dynWorld.dynamicObjects.values():
+        length = min(len(dynObj.ax_vec), len(dynObj.ay_vec))
+        if length == 0:
+            continue
+
+        ax = np.array(dynObj.ax_vec[:length])
+        ay = np.array(dynObj.ay_vec[:length])
+        total_accel = np.sqrt(ax**2 + ay**2)
+        valid_accel = total_accel[~np.isnan(total_accel)]
+
+        if len(valid_accel) > 0:
+            max_accel = np.max(valid_accel)
+            if max_accel > acc_threshold:
+                high_accel_vehicles.append({
+                    "uuid": dynObj.UUID,
+                    "type": dynObj.type,
+                    "max_accel": max_accel,
+                })
+
+    if not high_accel_vehicles:
+        print(f"No vehicles with acceleration > {acc_threshold} m/s^2 found.")
+        return
+
+    sorted_vehicles = sorted(high_accel_vehicles, key=lambda x: x["max_accel"], reverse=True)
+
+    print(f"\n--- {len(sorted_vehicles)} vehicles with notable acceleration ---")
+    for item in sorted_vehicles:
+        print(f"Vehicle {item['uuid']} ({item['type']}): max {item['max_accel']:.2f} m/s^2")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python 03_high_acceleration.py <path_to_recording_folder> [threshold]")
+    else:
+        threshold = float(sys.argv[2]) if len(sys.argv) >= 3 else 3.0
+        detect_high_accelerations(sys.argv[1], threshold)

example_scripts/README.md

@@ -0,0 +1,48 @@
+# Example Scripts — Automatum Data Full Highway Dataset
+
+These scripts demonstrate how to work with the Automatum drone traffic data using the `openautomatumdronedata` Python library.
+
+## Prerequisites
+
+```bash
+pip install openautomatumdronedata numpy matplotlib
+```
+
+## Scripts
+
+| Script | Description |
+|--------|-------------|
+| `01_lane_changes.py` | Analyzes lane change frequency per vehicle and shows the top 10 |
+| `02_heatmap_density.py` | Creates a traffic density heatmap from all position data |
+| `03_high_acceleration.py` | Detects vehicles exceeding an acceleration threshold |
+
+## Usage
+
+All scripts take the path to a recording folder as their first argument:
+
+```bash
+python 01_lane_changes.py ../hw-a9-denkendorf-001-uuid
+python 02_heatmap_density.py ../hw-a9-appershofen-001-uuid
+python 03_high_acceleration.py ../hw-a9-brunn-001-uuid 3.0
+```
+
+## Processing All Recordings
+
+To batch-process all 114 recordings:
+
+```python
+import os
+from openautomatumdronedata.dataset import droneDataset
+
+base = "path/to/automatum_data_full_highway_drone_dataset"
+for folder in sorted(os.listdir(base)):
+    if folder.startswith("hw-"):
+        dataset = droneDataset(os.path.join(base, folder))
+        print(f"{folder}: {len(dataset.dynWorld)} vehicles")
+```
+
+## Learn More
+
+- [Full Documentation](https://openautomatumdronedata.readthedocs.io)
+- [Research Paper (IV2021)](../doc/IV21_Automatumd_Full_Drone_Dataset.pdf)
+- [Automatum Data Website](https://automatum-data.com)