Upload automatum_data_highway_with_ramps — Sample_Data, docs, examples, archive

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README.md

@@ -0,0 +1,316 @@
+---
+language:
+- en
+- de
+license: cc-by-nd-4.0
+tags:
+- autonomous-driving
+- traffic-analysis
+- trajectory-prediction
+- drone-data
+- automatum
+- open-drive
+- json
+- highway
+- ramps
+- on-ramp
+- off-ramp
+- openscenario
+pretty_name: "Automatum Data: Highway with Ramps Drone Dataset"
+task_categories:
+- time-series-forecasting
+- object-detection
+size_categories:
+- 1K<n<10K
+---
+
+![Automatum Data Logo](doc/automatum_logo.png)
+
+# Automatum Data: Highway with Ramps 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 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.
+
+![Illustration of Drone Data Extraction](doc/illustration.jpg)
+
+## 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 |
+
+![Highway with Ramps Scenario](doc/icon_ramps.jpg)
+
+## 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
+
+| | |
+|---|---|
+| ![Map](doc/map_kinding.jpg) | ![Trajectories](doc/trajectories_kinding.jpg) |
+
+| 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
+
+| | |
+|---|---|
+| ![Map](doc/map_brunnthal.jpg) | ![Trajectories](doc/trajectories_brunnthal.jpg) |
+
+| 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
+
+| | |
+|---|---|
+| ![Map](doc/map_feldkirchen.jpg) | ![Trajectories](doc/trajectories_feldkirchen.jpg) |
+
+| 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
+
+| | |
+|---|---|
+| ![Map](doc/map_allersberg.jpg) | ![Trajectories](doc/trajectories_allersberg.jpg) |
+
+| 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`
+
+![Vehicle Dynamics](doc/VehicleDynamics.png)
+
+### 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
+
+![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("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
+
+```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-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](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)

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+    "Ø Geschwindigkeit (km/h)": 123.5,
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+}

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+                        </roadMark>
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="1" type="shoulder">
+                        <width sOffset="0" a="1.686" b="0" c="1.41961944991598e-006" d="-1.40734034581059e-009" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </left>
+                <center>
+                    <lane id="0" type="driving" />
+                </center>
+                <right>
+                    <lane id="-1" type="shoulder">
+                        <width sOffset="0" a="1.628" b="0" c="-4.16598604928615e-006" d="4.12995204284603e-009" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-2" type="border">
+                        <width sOffset="0" a="1.235" b="0" c="-2.0164417548112e-005" d="6.4799820691117e-008" />
+                        <width sOffset="150" a="1" b="-0.0016753373677832" c="8.99550176289065e-006" d="-9.43220728772407e-009" />
+                        <roadMark sOffset="1e-007" type="solid" color="white" width="0.3" height="0">
+                            <type name="solid" width="0.3">
+                                <line length="0" space="0" tOffset="0" sOffset="0" width="0.3" />
+                            </type>
+                        </roadMark>
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-3" type="driving">
+                        <width sOffset="0" a="3.95" b="0" c="-3.82115233850921e-005" d="1.80669415159873e-007" />
+                        <width sOffset="150" a="3.7" b="0.000731728507763818" c="4.30897134368509e-005" d="-3.54485671842548e-007" />
+                        <width sOffset="200" a="3.8" b="0.0023820573126298" c="-1.00831373395313e-005" d="1.06703623448512e-008" />
+                        <roadMark sOffset="5.137" type="broken" color="standard" width="0.15" height="0">
+                            <type name="broken" width="0.15">
+                                <line length="6" space="11.92" tOffset="0" sOffset="0" width="0.15" />
+                            </type>
+                        </roadMark>
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-4" type="driving">
+                        <width sOffset="0" a="3.741" b="0" c="0" d="0" />
+                        <roadMark sOffset="5.312" type="broken" color="standard" width="0.15" height="0">
+                            <type name="broken" width="0.15">
+                                <line length="6" space="11.94" tOffset="0" sOffset="0" width="0.15" />
+                            </type>
+                        </roadMark>
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-5" type="driving">
+                        <width sOffset="0" a="3.97" b="0" c="0" d="0" />
+                        <roadMark sOffset="1e-007" type="solid" color="standard" width="0.3" height="0">
+                            <type name="solid" width="0.3">
+                                <line length="0" space="0" tOffset="0" sOffset="0" width="0.3" />
+                            </type>
+                        </roadMark>
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-6" type="stop">
+                        <width sOffset="0" a="3.087" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-7" type="border">
+                        <width sOffset="0" a="0.665" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-8" type="shoulder">
+                        <width sOffset="0" a="2.5" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </right>
+            </laneSection>
+        </lanes>
+    </road>
+</OpenDRIVE>

automatum_data_highway_with_ramps.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13653df10a5e8e87407259b8e26db35237e1e05915f652613860075a6a274a99
+size 1507190732

example_scripts/01_lane_changes.py

@@ -0,0 +1,54 @@
+"""
+Lane Change Analysis — Automatum Data Highway with Ramps 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 ../Highway-A9-Allersberg_9b82-9b822c8f-b3dc-4c5c-824d-2354203d0e7b
+"""
+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 Highway with Ramps 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 ../Highway-A9-Allersberg_9b82-9b822c8f-b3dc-4c5c-824d-2354203d0e7b
+
+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 Highway with Ramps 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 ../Highway-A9-Allersberg_9b82-9b822c8f-b3dc-4c5c-824d-2354203d0e7b 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 +1,32 @@
+# Example Scripts — Automatum Data Highway with Ramps 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 ../Highway-A9-Allersberg_9b82-9b822c8f-b3dc-4c5c-824d-2354203d0e7b
+python 02_heatmap_density.py ../Highway-A8-Brunnthal_Sued_96cf-96cf7529-ddef-4685-bd27-a9568b74e501
+python 03_high_acceleration.py ../Highway-A99-FeldkirchenNord_0a45-0a45233c-5a85-466b-af41-84a799752af4 3.0
+```
+
+## Learn More
+
+- [Full Documentation](https://openautomatumdronedata.readthedocs.io)
+- [Automatum Data Website](https://automatum-data.com)