Upload automatum_data_crossing — Sample_Data, docs, examples, archive

.gitattributes

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README.md

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+---
+language:
+- en
+- de
+license: cc-by-nd-4.0
+tags:
+- autonomous-driving
+- traffic-analysis
+- trajectory-prediction
+- drone-data
+- automatum
+- open-drive
+- json
+- t-crossing
+- intersection
+- openscenario
+pretty_name: "Automatum Data: T-Crossing Drone Dataset"
+task_categories:
+- time-series-forecasting
+- object-detection
+size_categories:
+- 1K<n<10K
+---
+
+![Automatum Data Logo](doc/automatum_logo.png)
+
+# Automatum Data: T-Crossing 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 T-Crossing Dataset** contains high-precision movement data of traffic participants (cars, trucks, vans) extracted from drone recordings at T-shaped intersections in Bavaria, Germany. The data is captured from a bird's eye view and provides complete trajectories with velocities, accelerations, lane assignments, and object relationships.
+
+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** | T-Crossing / Intersection |
+| **Recordings** | 2 |
+| **Total Duration** | ~30 minutes (0.49 hours) |
+| **Total Distance** | 108.8 km |
+| **Total Vehicles Tracked** | 683 |
+| **Vehicle Types** | 623 Cars, 47 Trucks, 13 Vans |
+| **Max Trajectory Length** | 160.3 m |
+| **Coordinate System** | UTM Zone 32U |
+| **FPS** | 29.97 |
+| **License** | CC BY-ND 4.0 |
+
+![T-Crossing Scenario](doc/icon_crossing.jpg)
+
+## Repository Structure
+
+```
+automatum-data-crossing/
+├── 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
+│   └── T-Crossing--GaimersheimStadtweg_e2e6-.../
+│       ├── dynamicWorld.json
+│       ├── staticWorld.xodr
+│       ├── recording.html
+│       └── img/
+└── automatum_data_crossing.zip        # All recordings as archive
+```
+
+> **Quick Preview:** Browse `Sample_Data/` to explore the data structure before downloading the full archive. The sample recording can be loaded directly with the `openautomatumdronedata` Python library.
+
+## Recording Overview
+
+### 1. T-Crossing Gaimersheim Stadtweg
+
+| | |
+|---|---|
+| ![Map](doc/map_gaimersheim.jpg) | ![Trajectories](doc/trajectories_gaimersheim.jpg) |
+
+| KPI | Value |
+|-----|-------|
+| Trajectories | 299 |
+| Duration | 650.7 s (~10.8 min) |
+| Traffic Flow | 1,654.3 veh/h |
+| Traffic Density | 40.2 veh/km |
+| Avg. Speed | 41.2 km/h |
+| Max. Speed | 109.4 km/h |
+| Max. Acceleration | 4.7 m/s² |
+| Location | 48.7882°N, 11.3855°E |
+
+### 2. T-Crossing St2214 Dünzlau Umgehung
+
+| | |
+|---|---|
+| ![Map](doc/map_duenzlau.jpg) | ![Trajectories](doc/trajectories_duenzlau.jpg) |
+
+| KPI | Value |
+|-----|-------|
+| Trajectories | 384 |
+| Duration | 1,125.6 s (~18.8 min) |
+| Traffic Flow | 1,228.1 veh/h |
+| Traffic Density | 22.1 veh/km |
+| Avg. Speed | 55.6 km/h |
+| Max. Speed | 110.4 km/h |
+| Max. Acceleration | 5.8 m/s² |
+| Location | 48.7762°N, 11.3196°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
+- Lane definitions with widths and types
+- Junction 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("T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2")
+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}) — x={obj.x_vec[0]:.1f}, y={obj.y_vec[0]:.1f}")
+```
+
+### 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-crossing",
+    repo_type="dataset",
+    allow_patterns=["Sample_Data/**"]
+)
+
+# Option 2: Download the full archive
+archive = hf_hub_download(
+    repo_id="AutomatumData/automatum-data-crossing",
+    filename="automatum_data_crossing.zip",
+    repo_type="dataset"
+)
+# Extract
+with zipfile.ZipFile(archive, 'r') as z:
+    z.extractall("automatum_data_crossing")
+
+# Load with openautomatumdronedata
+from openautomatumdronedata.dataset import droneDataset
+dataset = droneDataset("automatum_data_crossing/T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2")
+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
+- **`04_export_objects.py`** — Export per-vehicle JSON files with surrounding object data
+
+## 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 |
+| **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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+            </geometry>
+        </planView>
+        <lanes>
+            <laneSection s="0">
+                <left>
+                    <lane id="2" type="driving">
+                        <width sOffset="0" a="4.381" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="1" type="driving">
+                        <width sOffset="0" a="0.5" b="1.38777878078145e-017" c="0.002125" d="-2.1875e-005" />
+                        <width sOffset="40" a="2.5" b="0.065" c="-0.0005" d="-0.0001" />
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+                        <width sOffset="166" a="2" b="-0.0504299708439463" c="-0.000282969915890417" d="2.88910671443846e-005" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </left>
+                <center>
+                    <lane id="0" type="driving">
+                        <roadMark sOffset="1e-007" type="solid" color="white" width="0.15" height="0">
+                            <type name="solid" width="0.15">
+                                <line length="0" space="0" tOffset="0" sOffset="0" width="0.15" />
+                            </type>
+                        </roadMark>
+                    </lane>
+                </center>
+                <right>
+                    <lane id="-1" type="driving">
+                        <width sOffset="0" a="3.698" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </right>
+            </laneSection>
+        </lanes>
+    </road>
+    <road name="object 66" length="61.8143834107681" id="5" junction="-1" rule="RHT">
+        <link>
+            <predecessor elementType="junction" elementId="6" />
+        </link>
+        <type s="0" type="rural" country="DE">
+            <speed max="100" unit="km/h" />
+        </type>
+        <planView>
+            <geometry s="0" x="-24.56" y="-1.773" hdg="-1.89551483412844" length="9.65245624431065">
+                <paramPoly3 aU="0" bU="0.999803567789528" cU="-4.91538290343498e-005" dU="-3.80539486688071e-006" aV="0" bV="-5.55111512312578e-016" cV="0.00562248449695805" dV="-0.000145589854924663" pRange="arcLength" />
+            </geometry>
+            <geometry s="9.65245624431065" x="-27.264" y="-11.037" hdg="-1.82762134955448" length="12.6495636682623">
+                <paramPoly3 aU="0" bU="1.0002840218728" cU="-6.35803129107505e-005" dU="2.89350302280312e-006" aV="0" bV="5.55111512312578e-016" cV="0.00141468947235499" dV="-4.95470616208213e-005" pRange="arcLength" />
+            </geometry>
+            <geometry s="22.3020199125729" x="-30.355" y="-23.303" hdg="-1.81561658438663" length="39.5123634981952">
+                <paramPoly3 aU="0" bU="0.999922077013402" cU="4.06148201549196e-005" dU="-1.20689808684295e-006" aV="0" bV="2.22044604925031e-016" cV="-0.000465878145381665" dV="2.2328352790449e-005" pRange="arcLength" />
+            </geometry>
+        </planView>
+        <lanes>
+            <laneSection s="0">
+                <left>
+                    <lane id="2" type="driving">
+                        <width sOffset="0" a="5" b="-0.03" c="-0.00180437051462216" d="4.33748205848865e-005" />
+                        <width sOffset="25" a="3.8" b="-0.0388907371344459" c="0.00144874102924433" d="-1.66699468772913e-005" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="1" type="border">
+                        <width sOffset="0" a="1.2" b="-0.1" c="0.00266666666666667" d="-2.22222222222222e-005" />
+                        <width sOffset="30" a="0" b="0" c="0" d="0" />
+                        <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="border">
+                        <width sOffset="0" a="1.4" b="-0.1" c="0.002" d="-7.40740740740741e-006" />
+                        <width sOffset="30" a="0" b="0" c="0" d="0" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                    <lane id="-2" type="driving">
+                        <width sOffset="0" a="5.3" b="-0.03" c="5.29356165377947e-006" d="-3.41174246615117e-006" />
+                        <width sOffset="25" a="4.5" b="-0.0361323390413445" c="-0.000250587123307558" d="1.34245326241329e-005" />
+                        <material sOffset="0" friction="1" />
+                        <speed sOffset="0" max="100" unit="km/h" />
+                    </lane>
+                </right>
+            </laneSection>
+        </lanes>
+    </road>
+    <junction name="object 126" id="6" type="virtual">
+        <connection id="0" incomingRoad="5" connectingRoad="0" contactPoint="start" type="default">
+            <laneLink from="2" to="-1" />
+        </connection>
+        <connection id="1" incomingRoad="4" connectingRoad="1" contactPoint="start" type="default">
+            <laneLink from="-1" to="-1" />
+        </connection>
+        <connection id="2" incomingRoad="5" connectingRoad="2" contactPoint="start" type="default">
+            <laneLink from="2" to="-1" />
+        </connection>
+        <connection id="3" incomingRoad="4" connectingRoad="3" contactPoint="start" type="default">
+            <laneLink from="1" to="-1" />
+        </connection>
+    </junction>
+</OpenDRIVE>

automatum_data_crossing.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea2c528fafdb306ea20686065b15c39ae2332342594a0f8031477bdd8f91f19f
+size 33479907

example_scripts/01_lane_changes.py

@@ -0,0 +1,54 @@
+"""
+Lane Change Analysis — Automatum Data T-Crossing 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 ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2
+"""
+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 T-Crossing 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 ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2
+
+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=(12, 8))
+    plt.style.use("dark_background")
+    plt.hist2d(all_x, all_y, bins=(200, 200), cmap="inferno", cmin=1)
+    plt.colorbar(label="Traffic density (data points)")
+    plt.title("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 T-Crossing 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 ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 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 +1,33 @@
+# Example Scripts — Automatum Data T-Crossing 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 |
+| `example_export_objects.py` | Exports per-vehicle JSON files with surrounding object data |
+
+## Usage
+
+All scripts take the path to a recording folder as their first argument:
+
+```bash
+python 01_lane_changes.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2
+python 02_heatmap_density.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2
+python 03_high_acceleration.py ../T-Crossing--GaimersheimStadtweg_e2e6-e2e6f4bb-4668-4654-ac7e-bcd90c9df4c2 3.0
+```
+
+## Learn More
+
+- [Full Documentation](https://openautomatumdronedata.readthedocs.io)
+- [Automatum Data Website](https://automatum-data.com)

example_scripts/example_export_objects.py

@@ -0,0 +1,164 @@
+"""
+    This is a demo script that was used to take the default dynamicWorld.json and extract every vehicle with its related objects and sorts them 
+    to a new json file for each ego vehicle.
+    This allows an analysis of each maneuver without accessing other vehicles in the dynamicWorld. 
+    Please note that this will generate a lot of redundant information. 
+
+"""
+from openautomatumdronedata.dataset import droneDataset
+import json 
+import os
+import shutil
+import sys
+
+# Get all present recording folders in the current dataset 
+dataset_folders = list()
+current_path = os.path.abspath(os.path.join(__file__ ,"../.."))
+for item in os.listdir(current_path):
+    if os.path.isdir(os.path.join(current_path, item)) and item != "img":
+        dataset_folders.append(item)
+
+
+
+for recording_folder in dataset_folders:
+    path = os.path.join(current_path, recording_folder)
+
+    # Create an output folder
+    export_path = os.path.join(current_path, recording_folder, "export_single_objects")
+    if not os.path.exists(export_path):
+        os.mkdir(export_path)
+
+    # Now we open each dataset and create a droneDataset object for it
+    dataset = droneDataset(path)
+
+    # Here we access the dynamic world, the global JSON file containing all recording infromation
+    dynWorld = dataset.dynWorld  
+
+    # Here we open the plain JSON file without the automatum pip utility in parallel
+    f = open(os.path.join(path, "dynamicWorld.json"))
+    json_dict = json.load(f)
+
+    # Create a new dict to store all agregated values in
+    relation_dict = dict()
+
+    # Lets take every object (car, truck, etc.) from the plain JSON file and crate a new JSON containing only this object with all its surrounding objects
+    for object in json_dict["objects"]:
+        """ 
+            Now we access the object_relation_dict_list, ttc_dict and tth_dict of the object to see which objects are the surrounding ones:
+                "object_relation_dict_list": [
+                {
+                    "front_ego": null,
+                    "behind_ego": "32499e60-30e9-4f41-8dc4-8699364db5dc",
+                    "front_left": null,
+                    "behind_left": null,
+                    "front_right": "3e67c856-116a-4af5-96cc-39f5002f71a0",
+                    "behind_right": "3002eaf3-a545-4e56-aa31-557f25e79643"
+                },
+                ...
+
+                "ttc_dict_vec": [
+                {
+                    "front_ego": -1,
+                    "behind_ego": null,
+                    "front_left": null,
+                    "behind_left": null,
+                    "front_right": 477.62466112341815,
+                    "behind_right": null
+                },
+                ...
+
+                "tth_dict_vec": [
+                {
+                    "front_ego": null,
+                    "behind_ego": 0.380621726114513,
+                    "front_left": null,
+                    "behind_left": null,
+                    "front_right": -1,
+                    "behind_right": 3.687973804225473
+                },
+                ...
+        """
+        for i, (object_relation_dict, ttc_dict, tth_dict, lat_dict, long_dict) in enumerate(zip(object["object_relation_dict_list"], object["ttc_dict_vec"], object["tth_dict_vec"], object["lat_dist_dict_vec"], object["long_dist_dict_vec"])):
+            time_stamp = object["time"][i]
+            for key in object_relation_dict.keys():  # key = "front_ego", relation = "UUID of the object in this position" for example
+                relation = object_relation_dict[key]
+                if relation is not None:  # Check if there is an object at this position at all
+                    relation_object = dynWorld.get_dynObj_by_UUID(relation)  # Access the object with the automatum utility by its UUID
+                    time_idx = relation_object.next_index_of_specific_time(time_stamp)  # Get the time index of the object for the time stamp of our current ego vehicle we generate the new JSON for
+
+
+                    # Copy all values from this object at the specific time
+                    relation_dict["UUID"] = relation_object.UUID
+                    relation_dict["length"] = relation_object.length
+                    relation_dict["width"] = relation_object.width
+                    relation_dict["x"] = relation_object.x_vec[time_idx]
+                    relation_dict["y"] = relation_object.y_vec[time_idx]
+                    relation_dict["vx"] = relation_object.vx_vec[time_idx]
+                    relation_dict["vy"] = relation_object.vy_vec[time_idx]
+                    relation_dict["ax"] = relation_object.ax_vec[time_idx]
+                    relation_dict["ay"] = relation_object.ay_vec[time_idx]
+                    relation_dict["jerk_x"] = relation_object.vx_vec[time_idx]
+                    relation_dict["jerk_y"] = relation_object.vx_vec[time_idx]
+                    relation_dict["curvature"] = relation_object.vx_vec[time_idx]
+                    relation_dict["psi"] = relation_object.psi_vec[time_idx]
+                    relation_dict["lane_id"] = relation_object.lane_id_vec[time_idx]
+                    relation_dict["road_id"] = relation_object.road_id_vec[time_idx]
+                    relation_dict["road_type"] = relation_object.vx_vec[time_idx]
+                    relation_dict["distance_left_lane_marking"] = relation_object.distance_left_lane_marking[time_idx]
+                    relation_dict["distance_right_lane_marking"] = relation_object.distance_right_lane_marking[time_idx]
+                    relation_dict["ttc"] = ttc_dict[key]
+                    relation_dict["tth"] = tth_dict[key]
+                    relation_dict["lat_dist"] = lat_dict[key]
+                    relation_dict["long_dist"] = long_dict[key]
+
+                else:
+                    relation_dict = None
+
+                 
+                """
+                    Now we replace the initial single UUID of the object with all information we accumulated about the object behind the UUID
+
+                        "object_relation_dict_list": [
+                        {
+                            "front_left": 0decabdc-fa4f-4f25-93ed-88eed734bba0,
+                        ...
+
+                        "object_relation_dict_list": [
+                        {
+                            "front_left": {
+                                "UUID": "0decabdc-fa4f-4f25-93ed-88eed734bba0",
+                                "length": 4.172288426073395,
+                                "width": 1.8141249203213998,
+                                "vx": 46.54388406290268,
+                                "vy": 0.005328263922638854,
+                                "ax": 0.5608367460027531,
+                                "ay": -0.5516711364613421,
+                                "psi": -0.5643746012832805,
+                                "x": 47.834595023288536,
+                                "y": -32.82371510377445,
+                                "lane_id": 3,
+                                "road_id": 0,
+                                "distance_left_lane_marking": 2.3102357971463827,
+                                "distance_right_lane_marking": 1.6230526113559351
+                            },
+                        ---
+
+                """
+                object["object_relation_dict_list"][i][key] = relation_dict
+
+                relation_dict = dict()  # Delete the dict for the next object
+
+
+        # Delete redundant information
+        del object["ttc_dict_vec"]
+        del object["tth_dict_vec"]
+        del object["lat_dist_dict_vec"]
+        del object["long_dist_dict_vec"]
+
+        # Finally we save each object as its own JSON    
+        with open(os.path.join(export_path, object["UUID"] + ".json"), "w") as outfile:
+            json.dump(object, outfile)
+        print("Successfully exported object %s" % object["UUID"])
+
+
+