Datasets:
Upload automatum_data_roundaboud — Sample_Data, docs, examples, archive
Browse files- .gitattributes +1 -0
- README.md +274 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d.html +0 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/dynamicWorld.json +3 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_centerImg_thumb.jpg +3 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_map.jpg +3 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_trajectories.jpg +3 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/kpis.json +10 -0
- Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/staticWorld.xodr +1109 -0
- automatum.dataset.html +0 -0
- automatum_data_roundabout.zip +3 -0
- doc/VehicleDynamics.png +3 -0
- doc/automatum_logo.png +3 -0
- doc/icon_roundabout.jpg +3 -0
- doc/illustration.jpg +3 -0
- doc/lane_distance.png +3 -0
- doc/map_ingolstadt.jpg +3 -0
- doc/map_koesching.jpg +3 -0
- doc/point_to_lane_assignement_Sans.png +3 -0
- doc/static_world_fig_02.png +3 -0
- doc/trajectories_ingolstadt.jpg +3 -0
- doc/trajectories_koesching.jpg +3 -0
- doc/ttc.png +3 -0
- example_scripts/01_lane_changes.py +54 -0
- example_scripts/02_heatmap_density.py +58 -0
- example_scripts/03_high_acceleration.py +61 -0
- example_scripts/README.md +32 -0
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Roundabout-KoeschingerTor-Koesching_19bb-19bbabb9-6c81-4a15-af8c-50fc251a248d/dynamicWorld.json filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/dynamicWorld.json filter=lfs diff=lfs merge=lfs -text
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| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
- de
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| 5 |
+
license: cc-by-nd-4.0
|
| 6 |
+
tags:
|
| 7 |
+
- autonomous-driving
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| 8 |
+
- traffic-analysis
|
| 9 |
+
- trajectory-prediction
|
| 10 |
+
- drone-data
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| 11 |
+
- automatum
|
| 12 |
+
- open-drive
|
| 13 |
+
- json
|
| 14 |
+
- roundabout
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| 15 |
+
- intersection
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| 16 |
+
- openscenario
|
| 17 |
+
pretty_name: "Automatum Data: Roundabout Drone Dataset"
|
| 18 |
+
task_categories:
|
| 19 |
+
- time-series-forecasting
|
| 20 |
+
- object-detection
|
| 21 |
+
size_categories:
|
| 22 |
+
- 100<n<1K
|
| 23 |
+
---
|
| 24 |
+
|
| 25 |
+

|
| 26 |
+
|
| 27 |
+
# Automatum Data: Roundabout Drone Dataset
|
| 28 |
+
|
| 29 |
+
[](https://automatum-data.com)
|
| 30 |
+
[](https://openautomatumdronedata.readthedocs.io)
|
| 31 |
+
[](https://pypi.org/project/openautomatumdronedata/)
|
| 32 |
+
[](https://creativecommons.org/licenses/by-nd/4.0/)
|
| 33 |
+
|
| 34 |
+
## Introduction
|
| 35 |
+
|
| 36 |
+
The **Automatum Data Roundabout Dataset** contains high-precision movement data of traffic participants (cars, trucks, vans) extracted from drone recordings at roundabout intersections in Bavaria, Germany. Captured from a bird's eye view, the dataset provides complete trajectories with velocities, accelerations, lane assignments, and object relationships — uniquely suited for roundabout behavior modeling.
|
| 37 |
+
|
| 38 |
+
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.
|
| 39 |
+
|
| 40 |
+

|
| 41 |
+
|
| 42 |
+
## Dataset at a Glance
|
| 43 |
+
|
| 44 |
+
| Metric | Value |
|
| 45 |
+
|--------|-------|
|
| 46 |
+
| **Scenario Type** | Roundabout |
|
| 47 |
+
| **Recordings** | 2 |
|
| 48 |
+
| **Total Duration** | ~7 minutes (0.12 hours) |
|
| 49 |
+
| **Total Distance** | 26.0 km |
|
| 50 |
+
| **Total Vehicles Tracked** | 212 |
|
| 51 |
+
| **Vehicle Types** | 207 Cars, 3 Trucks, 2 Vans |
|
| 52 |
+
| **Max Trajectory Length** | 125.5 m |
|
| 53 |
+
| **Coordinate System** | UTM Zone 32U |
|
| 54 |
+
| **FPS** | 29.97 |
|
| 55 |
+
| **License** | CC BY-ND 4.0 |
|
| 56 |
+
|
| 57 |
+

|
| 58 |
+
|
| 59 |
+
## Repository Structure
|
| 60 |
+
|
| 61 |
+
```
|
| 62 |
+
automatum-data-roundabout/
|
| 63 |
+
├── README.md # This file
|
| 64 |
+
├── doc/ # Documentation images, logo, technical diagrams
|
| 65 |
+
├── example_scripts/ # Ready-to-use Python analysis scripts
|
| 66 |
+
├── Sample_Data/ # One recording unpacked for quick preview
|
| 67 |
+
│ └── Roundabout-St2231-IngolstadtVOC_e63d-.../
|
| 68 |
+
│ ├── dynamicWorld.json
|
| 69 |
+
│ ├── staticWorld.xodr
|
| 70 |
+
│ ├── recording.html
|
| 71 |
+
│ └── img/
|
| 72 |
+
└── automatum_data_roundabout.zip # All recordings as archive
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
> **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.
|
| 76 |
+
|
| 77 |
+
## Recording Overview
|
| 78 |
+
|
| 79 |
+
### 1. Roundabout St2231 Ingolstadt VOC
|
| 80 |
+
|
| 81 |
+
| | |
|
| 82 |
+
|---|---|
|
| 83 |
+
|  |  |
|
| 84 |
+
|
| 85 |
+
| KPI | Value |
|
| 86 |
+
|-----|-------|
|
| 87 |
+
| Trajectories | 148 |
|
| 88 |
+
| Duration | 288.6 s (~4.8 min) |
|
| 89 |
+
| Traffic Flow | 1,846.0 veh/h |
|
| 90 |
+
| Traffic Density | 139.9 veh/km |
|
| 91 |
+
| Avg. Speed | 13.2 km/h |
|
| 92 |
+
| Max. Speed | 54.6 km/h |
|
| 93 |
+
| Max. Acceleration | 3.9 m/s² |
|
| 94 |
+
| Location | 48.7841°N, 11.4821°E |
|
| 95 |
+
|
| 96 |
+
### 2. Roundabout Köschinger Tor, Kösching
|
| 97 |
+
|
| 98 |
+
| | |
|
| 99 |
+
|---|---|
|
| 100 |
+
|  |  |
|
| 101 |
+
|
| 102 |
+
| KPI | Value |
|
| 103 |
+
|-----|-------|
|
| 104 |
+
| Trajectories | 64 |
|
| 105 |
+
| Duration | 133.5 s (~2.2 min) |
|
| 106 |
+
| Traffic Flow | 1,726.3 veh/h |
|
| 107 |
+
| Traffic Density | 65.1 veh/km |
|
| 108 |
+
| Avg. Speed | 26.5 km/h |
|
| 109 |
+
| Max. Speed | 55.8 km/h |
|
| 110 |
+
| Max. Acceleration | 4.2 m/s² |
|
| 111 |
+
| Location | 48.8087°N, 11.4804°E |
|
| 112 |
+
|
| 113 |
+
## Data Structure
|
| 114 |
+
|
| 115 |
+
Each recording folder contains:
|
| 116 |
+
|
| 117 |
+
```
|
| 118 |
+
recording_folder/
|
| 119 |
+
├── dynamicWorld.json # Trajectories, velocities, accelerations, bounding boxes
|
| 120 |
+
├── staticWorld.xodr # Road geometry in OpenDRIVE format
|
| 121 |
+
├── recording_name.html # Interactive metadata overview (Bokeh)
|
| 122 |
+
└── img/
|
| 123 |
+
├── kpis.json # Key performance indicators
|
| 124 |
+
├── *_map.jpg # Aerial map view
|
| 125 |
+
├── *_trajectories.jpg # Trajectory visualization
|
| 126 |
+
└── *_centerImg_thumb.jpg # Center frame thumbnail
|
| 127 |
+
```
|
| 128 |
+
|
| 129 |
+
### dynamicWorld.json
|
| 130 |
+
|
| 131 |
+
The core data file contains for each tracked vehicle:
|
| 132 |
+
|
| 133 |
+
- **Position vectors**: `x_vec`, `y_vec` — UTM coordinates over time
|
| 134 |
+
- **Velocity vectors**: `vx_vec`, `vy_vec` — in m/s
|
| 135 |
+
- **Acceleration vectors**: `ax_vec`, `ay_vec` — in m/s²
|
| 136 |
+
- **Jerk vectors**: `jerk_x_vec`, `jerk_y_vec`
|
| 137 |
+
- **Heading**: `psi_vec` — orientation angle
|
| 138 |
+
- **Lane assignment**: `lane_id_vec`, `road_id_vec` — linked to XODR
|
| 139 |
+
- **Object dimensions**: `length`, `width`
|
| 140 |
+
- **Object relationships**: `object_relation_dict_list` — front/behind/left/right neighbors
|
| 141 |
+
- **Safety metrics**: `ttc_dict_vec` (Time-to-Collision), `tth_dict_vec` (Time-to-Headway)
|
| 142 |
+
- **Lane distances**: `distance_left_lane_marking`, `distance_right_lane_marking`
|
| 143 |
+
|
| 144 |
+

|
| 145 |
+
|
| 146 |
+
### staticWorld.xodr
|
| 147 |
+
|
| 148 |
+
OpenDRIVE 1.6 format file defining:
|
| 149 |
+
|
| 150 |
+
- Road network topology and geometry
|
| 151 |
+
- Lane definitions with widths and types
|
| 152 |
+
- Junction configurations (roundabout-specific)
|
| 153 |
+
- Speed limits and road markings
|
| 154 |
+
|
| 155 |
+

|
| 156 |
+
|
| 157 |
+
### Key Metrics Explained
|
| 158 |
+
|
| 159 |
+

|
| 160 |
+

|
| 161 |
+

|
| 162 |
+
|
| 163 |
+
## Quick Start
|
| 164 |
+
|
| 165 |
+
### Installation
|
| 166 |
+
|
| 167 |
+
```bash
|
| 168 |
+
pip install openautomatumdronedata
|
| 169 |
+
```
|
| 170 |
+
|
| 171 |
+
### Load and Explore
|
| 172 |
+
|
| 173 |
+
```python
|
| 174 |
+
from openautomatumdronedata.dataset import droneDataset
|
| 175 |
+
import os
|
| 176 |
+
|
| 177 |
+
# Point to one recording folder
|
| 178 |
+
path = os.path.abspath("Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d")
|
| 179 |
+
dataset = droneDataset(path)
|
| 180 |
+
|
| 181 |
+
# Access dynamic world
|
| 182 |
+
dynWorld = dataset.dynWorld
|
| 183 |
+
|
| 184 |
+
print(f"UUID: {dynWorld.UUID}")
|
| 185 |
+
print(f"Duration: {dynWorld.maxTime:.1f} seconds")
|
| 186 |
+
print(f"Frames: {dynWorld.frame_count}")
|
| 187 |
+
print(f"Vehicles: {len(dynWorld)}")
|
| 188 |
+
|
| 189 |
+
# Get all vehicles visible at t=1.0s
|
| 190 |
+
objects = dynWorld.get_list_of_dynamic_objects_for_specific_time(1.0)
|
| 191 |
+
for obj in objects[:5]:
|
| 192 |
+
print(f" {obj.UUID} ({obj.type}) — x={obj.x_vec[0]:.1f}, y={obj.y_vec[0]:.1f}")
|
| 193 |
+
```
|
| 194 |
+
|
| 195 |
+
### Using with Hugging Face
|
| 196 |
+
|
| 197 |
+
```python
|
| 198 |
+
from huggingface_hub import snapshot_download, hf_hub_download
|
| 199 |
+
import zipfile, os
|
| 200 |
+
|
| 201 |
+
# Option 1: Download only the sample for a quick look
|
| 202 |
+
local_path = snapshot_download(
|
| 203 |
+
repo_id="AutomatumData/automatum-data-roundabout",
|
| 204 |
+
repo_type="dataset",
|
| 205 |
+
allow_patterns=["Sample_Data/**"]
|
| 206 |
+
)
|
| 207 |
+
|
| 208 |
+
# Option 2: Download the full archive
|
| 209 |
+
archive = hf_hub_download(
|
| 210 |
+
repo_id="AutomatumData/automatum-data-roundabout",
|
| 211 |
+
filename="automatum_data_roundabout.zip",
|
| 212 |
+
repo_type="dataset"
|
| 213 |
+
)
|
| 214 |
+
# Extract
|
| 215 |
+
with zipfile.ZipFile(archive, 'r') as z:
|
| 216 |
+
z.extractall("automatum_data_roundabout")
|
| 217 |
+
|
| 218 |
+
# Load with openautomatumdronedata
|
| 219 |
+
from openautomatumdronedata.dataset import droneDataset
|
| 220 |
+
dataset = droneDataset("automatum_data_roundabout/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d")
|
| 221 |
+
print(f"Vehicles: {len(dataset.dynWorld)}")
|
| 222 |
+
```
|
| 223 |
+
|
| 224 |
+
## Example Scripts
|
| 225 |
+
|
| 226 |
+
See the `example_scripts/` folder for ready-to-use analysis scripts:
|
| 227 |
+
|
| 228 |
+
- **`01_lane_changes.py`** — Analyze lane change behavior across all vehicles
|
| 229 |
+
- **`02_heatmap_density.py`** — Generate traffic density heatmaps
|
| 230 |
+
- **`03_high_acceleration.py`** — Detect high-acceleration events
|
| 231 |
+
|
| 232 |
+
## Comparison with Established Datasets
|
| 233 |
+
|
| 234 |
+
| Feature | Automatum Data | highD | NGSIM |
|
| 235 |
+
|---------|---------------|-------|-------|
|
| 236 |
+
| **Data Format** | JSON + OpenDRIVE XODR | CSV + XML | CSV |
|
| 237 |
+
| **Road Geometry** | OpenDRIVE 1.6 standard | Simple annotations | Basic annotations |
|
| 238 |
+
| **Coordinate System** | UTM world coordinates | Local coordinates | Local coordinates |
|
| 239 |
+
| **Object Relationships** | Built-in (TTC, TTH, distances) | Must compute | Must compute |
|
| 240 |
+
| **Velocity Error** | < 0.2% (validated) | < 10 cm positional | Known issues |
|
| 241 |
+
| **Python Library** | `openautomatumdronedata` | Custom scripts | Custom scripts |
|
| 242 |
+
| **OpenSCENARIO** | Available on request | No | No |
|
| 243 |
+
|
| 244 |
+
## Research Use & Extended Data Pool
|
| 245 |
+
|
| 246 |
+
**These publicly available datasets are intended exclusively for research purposes.**
|
| 247 |
+
|
| 248 |
+
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:
|
| 249 |
+
|
| 250 |
+
**[automatum-data.com](https://automatum-data.com)**
|
| 251 |
+
|
| 252 |
+
## Citation
|
| 253 |
+
|
| 254 |
+
If you use this dataset in your research, please cite:
|
| 255 |
+
|
| 256 |
+
```bibtex
|
| 257 |
+
@inproceedings{spannaus2021automatum,
|
| 258 |
+
title={AUTOMATUM DATA: Drone-based highway dataset for development and validation of automated driving software},
|
| 259 |
+
author={Spannaus, Paul and Zechel, Peter and Lenz, Kilian},
|
| 260 |
+
booktitle={IEEE Intelligent Vehicles Symposium (IV)},
|
| 261 |
+
year={2021}
|
| 262 |
+
}
|
| 263 |
+
```
|
| 264 |
+
|
| 265 |
+
## License
|
| 266 |
+
|
| 267 |
+
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/).
|
| 268 |
+
|
| 269 |
+
## Contact
|
| 270 |
+
|
| 271 |
+
- **Website**: [automatum-data.com](https://automatum-data.com)
|
| 272 |
+
- **Email**: info@automatum-data.com
|
| 273 |
+
- **HuggingFace**: [AutomatumData](https://huggingface.co/AutomatumData)
|
| 274 |
+
- **Documentation**: [openautomatumdronedata.readthedocs.io](https://openautomatumdronedata.readthedocs.io)
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d.html
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/dynamicWorld.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:493070cc591946b689eaacb11adbda9e6007e2227fee110c3dbfae2b5ee17c5e
|
| 3 |
+
size 87519017
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_centerImg_thumb.jpg
ADDED
|
|
Git LFS Details
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_map.jpg
ADDED
|
Git LFS Details
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d_trajectories.jpg
ADDED
|
Git LFS Details
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/img/kpis.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"Anzahl Trajektorien": 148,
|
| 3 |
+
"Aufzeichnungsdauer (s)": 288.6,
|
| 4 |
+
"Verkehrsfluss (veh/h)": 1846.0,
|
| 5 |
+
"Verkehrsdichte (veh/km)": 139.9,
|
| 6 |
+
"Ø Trajektorienlänge (m)": 121.4,
|
| 7 |
+
"Ø Geschwindigkeit (km/h)": 13.2,
|
| 8 |
+
"Maximale Geschwindigkeit (km/h)": 54.6,
|
| 9 |
+
"Maximale Beschleunigung (m/s²)": 3.9
|
| 10 |
+
}
|
Sample_Data/Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d/staticWorld.xodr
ADDED
|
@@ -0,0 +1,1109 @@
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|
| 1 |
+
<?xml version="1.0" encoding="utf-8"?>
|
| 2 |
+
<OpenDRIVE xmlns="http://www.opendrive.org">
|
| 3 |
+
<header revMajor="1" revMinor="6" date="2023-17-01T12:44:22">
|
| 4 |
+
<userData code="Created by IPGRoad" value="LibVersion 11.0.1" />
|
| 5 |
+
</header>
|
| 6 |
+
<road name="object 155" length="14.3813805696253" id="0" junction="23" rule="RHT">
|
| 7 |
+
<link>
|
| 8 |
+
<predecessor elementType="road" elementId="16" elementS="69.445" elementDir="+" />
|
| 9 |
+
<successor elementType="road" elementId="17" contactPoint="end" />
|
| 10 |
+
</link>
|
| 11 |
+
<type s="0" type="rural" country="DE">
|
| 12 |
+
<speed max="100" unit="km/h" />
|
| 13 |
+
</type>
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| 14 |
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|
| 15 |
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<geometry s="0" x="16.0360897700138" y="-9.99369438253661" hdg="-1.08833468917599" length="14.3813805696253">
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| 16 |
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|
| 17 |
+
</geometry>
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| 18 |
+
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| 19 |
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|
| 20 |
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| 21 |
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| 23 |
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| 24 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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| 33 |
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| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
+
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|
| 39 |
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|
| 40 |
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|
| 41 |
+
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|
| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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</link>
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 53 |
+
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| 54 |
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| 55 |
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|
| 56 |
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| 57 |
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| 58 |
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|
| 59 |
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| 60 |
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| 61 |
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|
| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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|
| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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|
| 73 |
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| 74 |
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| 75 |
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|
| 76 |
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|
| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 89 |
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</geometry>
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| 90 |
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| 91 |
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|
| 92 |
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<elevation s="0" a="0" b="0" c="0" d="0" />
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| 93 |
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</elevationProfile>
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| 94 |
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|
| 95 |
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<laneOffset s="0" a="2.8615" b="0" c="-0.00370478087216085" d="0.000144356967856877" />
|
| 96 |
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<laneSection s="0">
|
| 97 |
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<center>
|
| 98 |
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<lane id="0" type="driving" />
|
| 99 |
+
</center>
|
| 100 |
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<right>
|
| 101 |
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<lane id="-1" type="driving">
|
| 102 |
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<link>
|
| 103 |
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<predecessor id="-1" />
|
| 104 |
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<successor id="2" />
|
| 105 |
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</link>
|
| 106 |
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<width sOffset="0" a="5.723" b="0" c="-0.0074095617443217" d="0.000288713935713755" />
|
| 107 |
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<material sOffset="0" friction="1" />
|
| 108 |
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<speed sOffset="0" max="100" unit="km/h" />
|
| 109 |
+
</lane>
|
| 110 |
+
</right>
|
| 111 |
+
</laneSection>
|
| 112 |
+
</lanes>
|
| 113 |
+
</road>
|
| 114 |
+
<road name="object 458" length="14.1060142060536" id="3" junction="24" rule="RHT">
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| 115 |
+
<link>
|
| 116 |
+
<predecessor elementType="road" elementId="20" contactPoint="end" />
|
| 117 |
+
<successor elementType="road" elementId="16" elementS="65.998" elementDir="+" />
|
| 118 |
+
</link>
|
| 119 |
+
<type s="0" type="rural" country="DE">
|
| 120 |
+
<speed max="100" unit="km/h" />
|
| 121 |
+
</type>
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| 122 |
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<planView>
|
| 123 |
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<geometry s="0" x="4.777750632168" y="3.21009444465841" hdg="-0.291073689430046" length="14.1060142060536">
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| 124 |
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|
| 125 |
+
</geometry>
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| 126 |
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|
| 128 |
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| 129 |
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</elevationProfile>
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| 130 |
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<lanes>
|
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<roadMark sOffset="1e-007" type="solid" color="white" width="0.12" height="0">
|
| 981 |
+
<type name="solid" width="0.12">
|
| 982 |
+
<line length="0" space="0" tOffset="-0" sOffset="0" width="0.12" />
|
| 983 |
+
</type>
|
| 984 |
+
</roadMark>
|
| 985 |
+
<material sOffset="0" friction="1" />
|
| 986 |
+
<speed sOffset="0" max="100" unit="km/h" />
|
| 987 |
+
</lane>
|
| 988 |
+
</left>
|
| 989 |
+
<center>
|
| 990 |
+
<lane id="0" type="driving">
|
| 991 |
+
<roadMark sOffset="1e-007" type="broken" color="white" width="0.12" height="0">
|
| 992 |
+
<type name="broken" width="0.12">
|
| 993 |
+
<line length="3" space="6" tOffset="0" sOffset="0" width="0.12" />
|
| 994 |
+
</type>
|
| 995 |
+
</roadMark>
|
| 996 |
+
</lane>
|
| 997 |
+
</center>
|
| 998 |
+
<right>
|
| 999 |
+
<lane id="-1" type="driving">
|
| 1000 |
+
<width sOffset="0" a="3.5" b="0" c="0" d="0" />
|
| 1001 |
+
<roadMark sOffset="1e-007" type="solid" color="white" width="0.12" height="0">
|
| 1002 |
+
<type name="solid" width="0.12">
|
| 1003 |
+
<line length="0" space="0" tOffset="0" sOffset="0" width="0.12" />
|
| 1004 |
+
</type>
|
| 1005 |
+
</roadMark>
|
| 1006 |
+
<material sOffset="0" friction="1" />
|
| 1007 |
+
<speed sOffset="0" max="100" unit="km/h" />
|
| 1008 |
+
</lane>
|
| 1009 |
+
<lane id="-2" type="border">
|
| 1010 |
+
<width sOffset="0" a="0.5" b="0" c="0" d="0" />
|
| 1011 |
+
<material sOffset="0" friction="1" />
|
| 1012 |
+
<speed sOffset="0" max="100" unit="km/h" />
|
| 1013 |
+
</lane>
|
| 1014 |
+
</right>
|
| 1015 |
+
</laneSection>
|
| 1016 |
+
</lanes>
|
| 1017 |
+
</road>
|
| 1018 |
+
<road name="object 478" length="44.764" id="22" junction="-1" rule="RHT">
|
| 1019 |
+
<type s="0" type="rural" country="DE">
|
| 1020 |
+
<speed max="100" unit="km/h" />
|
| 1021 |
+
</type>
|
| 1022 |
+
<planView>
|
| 1023 |
+
<geometry s="0" x="-55.565" y="54.985" hdg="-0.331612557878923" length="44.764">
|
| 1024 |
+
<line />
|
| 1025 |
+
</geometry>
|
| 1026 |
+
</planView>
|
| 1027 |
+
<lanes>
|
| 1028 |
+
<laneSection s="0">
|
| 1029 |
+
<left>
|
| 1030 |
+
<lane id="1" type="driving">
|
| 1031 |
+
<width sOffset="0" a="5.538" b="0" c="0" d="0" />
|
| 1032 |
+
<material sOffset="0" friction="1" />
|
| 1033 |
+
<speed sOffset="0" max="100" unit="km/h" />
|
| 1034 |
+
</lane>
|
| 1035 |
+
</left>
|
| 1036 |
+
<center>
|
| 1037 |
+
<lane id="0" type="driving" />
|
| 1038 |
+
</center>
|
| 1039 |
+
<right>
|
| 1040 |
+
<lane id="-1" type="driving">
|
| 1041 |
+
<width sOffset="0" a="3.807" b="0" c="0" d="0" />
|
| 1042 |
+
<material sOffset="0" friction="1" />
|
| 1043 |
+
<speed sOffset="0" max="100" unit="km/h" />
|
| 1044 |
+
</lane>
|
| 1045 |
+
</right>
|
| 1046 |
+
</laneSection>
|
| 1047 |
+
</lanes>
|
| 1048 |
+
</road>
|
| 1049 |
+
<junction name="object 145" id="23" type="virtual">
|
| 1050 |
+
<connection id="0" incomingRoad="16" connectingRoad="0" contactPoint="start" type="default">
|
| 1051 |
+
<laneLink from="-1" to="-1" />
|
| 1052 |
+
</connection>
|
| 1053 |
+
<connection id="1" incomingRoad="17" connectingRoad="1" contactPoint="start" type="default">
|
| 1054 |
+
<laneLink from="-3" to="-1" />
|
| 1055 |
+
</connection>
|
| 1056 |
+
</junction>
|
| 1057 |
+
<junction name="object 445" id="24" type="virtual">
|
| 1058 |
+
<connection id="0" incomingRoad="16" connectingRoad="2" contactPoint="start" type="default">
|
| 1059 |
+
<laneLink from="-1" to="-1" />
|
| 1060 |
+
</connection>
|
| 1061 |
+
<connection id="1" incomingRoad="20" connectingRoad="3" contactPoint="start" type="default">
|
| 1062 |
+
<laneLink from="-2" to="-1" />
|
| 1063 |
+
</connection>
|
| 1064 |
+
</junction>
|
| 1065 |
+
<junction name="object 493" id="25" type="virtual">
|
| 1066 |
+
<connection id="0" incomingRoad="21" connectingRoad="4" contactPoint="start" type="default">
|
| 1067 |
+
<laneLink from="-1" to="-1" />
|
| 1068 |
+
</connection>
|
| 1069 |
+
<connection id="1" incomingRoad="22" connectingRoad="5" contactPoint="start" type="default">
|
| 1070 |
+
<laneLink from="1" to="-1" />
|
| 1071 |
+
</connection>
|
| 1072 |
+
<connection id="2" incomingRoad="22" connectingRoad="6" contactPoint="start" type="default">
|
| 1073 |
+
<laneLink from="-1" to="-1" />
|
| 1074 |
+
</connection>
|
| 1075 |
+
<connection id="3" incomingRoad="21" connectingRoad="7" contactPoint="start" type="default">
|
| 1076 |
+
<laneLink from="-1" to="-1" />
|
| 1077 |
+
</connection>
|
| 1078 |
+
</junction>
|
| 1079 |
+
<junction name="object 541" id="26" type="virtual">
|
| 1080 |
+
<connection id="0" incomingRoad="21" connectingRoad="8" contactPoint="start" type="default">
|
| 1081 |
+
<laneLink from="1" to="-1" />
|
| 1082 |
+
</connection>
|
| 1083 |
+
<connection id="1" incomingRoad="20" connectingRoad="9" contactPoint="start" type="default">
|
| 1084 |
+
<laneLink from="-2" to="-1" />
|
| 1085 |
+
</connection>
|
| 1086 |
+
<connection id="2" incomingRoad="20" connectingRoad="10" contactPoint="start" type="default">
|
| 1087 |
+
<laneLink from="2" to="-1" />
|
| 1088 |
+
</connection>
|
| 1089 |
+
<connection id="3" incomingRoad="21" connectingRoad="11" contactPoint="start" type="default">
|
| 1090 |
+
<laneLink from="1" to="-1" />
|
| 1091 |
+
</connection>
|
| 1092 |
+
</junction>
|
| 1093 |
+
<junction name="object 212" id="27" type="virtual">
|
| 1094 |
+
<connection id="0" incomingRoad="16" connectingRoad="12" contactPoint="start" type="default">
|
| 1095 |
+
<laneLink from="-1" to="-1" />
|
| 1096 |
+
</connection>
|
| 1097 |
+
<connection id="1" incomingRoad="18" connectingRoad="13" contactPoint="start" type="default">
|
| 1098 |
+
<laneLink from="-2" to="-1" />
|
| 1099 |
+
</connection>
|
| 1100 |
+
</junction>
|
| 1101 |
+
<junction name="object 349" id="28" type="virtual">
|
| 1102 |
+
<connection id="0" incomingRoad="16" connectingRoad="14" contactPoint="start" type="default">
|
| 1103 |
+
<laneLink from="-1" to="-1" />
|
| 1104 |
+
</connection>
|
| 1105 |
+
<connection id="1" incomingRoad="19" connectingRoad="15" contactPoint="start" type="default">
|
| 1106 |
+
<laneLink from="-2" to="-1" />
|
| 1107 |
+
</connection>
|
| 1108 |
+
</junction>
|
| 1109 |
+
</OpenDRIVE>
|
automatum.dataset.html
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
automatum_data_roundabout.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3934ae688836a97794166943462a2510dc4a82ddba4aca3550fb185e679d08c3
|
| 3 |
+
size 30174289
|
doc/VehicleDynamics.png
ADDED
|
Git LFS Details
|
doc/automatum_logo.png
ADDED
|
Git LFS Details
|
doc/icon_roundabout.jpg
ADDED
|
|
Git LFS Details
|
doc/illustration.jpg
ADDED
|
Git LFS Details
|
doc/lane_distance.png
ADDED
|
Git LFS Details
|
doc/map_ingolstadt.jpg
ADDED
|
Git LFS Details
|
doc/map_koesching.jpg
ADDED
|
Git LFS Details
|
doc/point_to_lane_assignement_Sans.png
ADDED
|
Git LFS Details
|
doc/static_world_fig_02.png
ADDED
|
Git LFS Details
|
doc/trajectories_ingolstadt.jpg
ADDED
|
Git LFS Details
|
doc/trajectories_koesching.jpg
ADDED
|
Git LFS Details
|
doc/ttc.png
ADDED
|
Git LFS Details
|
example_scripts/01_lane_changes.py
ADDED
|
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
Lane Change Analysis — Automatum Data Roundabout Dataset
|
| 3 |
+
Analyzes lane change behavior across all vehicles in a recording.
|
| 4 |
+
|
| 5 |
+
Usage:
|
| 6 |
+
python 01_lane_changes.py <path_to_recording_folder>
|
| 7 |
+
|
| 8 |
+
Example:
|
| 9 |
+
python 01_lane_changes.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d
|
| 10 |
+
"""
|
| 11 |
+
import sys
|
| 12 |
+
import os
|
| 13 |
+
from openautomatumdronedata.dataset import droneDataset
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
def analyze_lane_changes(dataset_path):
|
| 17 |
+
print(f"Loading dataset from: {dataset_path}")
|
| 18 |
+
dataset = droneDataset(dataset_path)
|
| 19 |
+
dynWorld = dataset.dynWorld
|
| 20 |
+
|
| 21 |
+
print(f"Vehicles found: {len(dynWorld)}")
|
| 22 |
+
|
| 23 |
+
lane_change_counts = []
|
| 24 |
+
|
| 25 |
+
for dynObj in dynWorld.dynamicObjects.values():
|
| 26 |
+
lane_ids = dynObj.lane_id_vec
|
| 27 |
+
if len(lane_ids) == 0:
|
| 28 |
+
continue
|
| 29 |
+
|
| 30 |
+
changes = 0
|
| 31 |
+
current_lane = lane_ids[0]
|
| 32 |
+
for lane in lane_ids[1:]:
|
| 33 |
+
if lane != current_lane:
|
| 34 |
+
changes += 1
|
| 35 |
+
current_lane = lane
|
| 36 |
+
|
| 37 |
+
lane_change_counts.append({
|
| 38 |
+
"uuid": dynObj.UUID,
|
| 39 |
+
"type": dynObj.type,
|
| 40 |
+
"changes": changes,
|
| 41 |
+
})
|
| 42 |
+
|
| 43 |
+
sorted_by_changes = sorted(lane_change_counts, key=lambda x: x["changes"], reverse=True)
|
| 44 |
+
|
| 45 |
+
print("\n--- Top 10 vehicles with most lane changes ---")
|
| 46 |
+
for idx, item in enumerate(sorted_by_changes[:10]):
|
| 47 |
+
print(f"{idx+1}. Vehicle {item['uuid']} ({item['type']}): {item['changes']} lane changes")
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
if __name__ == "__main__":
|
| 51 |
+
if len(sys.argv) < 2:
|
| 52 |
+
print("Usage: python 01_lane_changes.py <path_to_recording_folder>")
|
| 53 |
+
else:
|
| 54 |
+
analyze_lane_changes(sys.argv[1])
|
example_scripts/02_heatmap_density.py
ADDED
|
@@ -0,0 +1,58 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
Traffic Density Heatmap — Automatum Data Roundabout Dataset
|
| 3 |
+
Generates a 2D heatmap of all vehicle positions over time.
|
| 4 |
+
|
| 5 |
+
Usage:
|
| 6 |
+
python 02_heatmap_density.py <path_to_recording_folder>
|
| 7 |
+
|
| 8 |
+
Example:
|
| 9 |
+
python 02_heatmap_density.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d
|
| 10 |
+
|
| 11 |
+
Output:
|
| 12 |
+
traffic_heatmap.png in the current directory
|
| 13 |
+
"""
|
| 14 |
+
import sys
|
| 15 |
+
import os
|
| 16 |
+
import numpy as np
|
| 17 |
+
import matplotlib.pyplot as plt
|
| 18 |
+
from openautomatumdronedata.dataset import droneDataset
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def generate_density_heatmap(dataset_path, output_filename="traffic_heatmap.png"):
|
| 22 |
+
print(f"Loading dataset from: {dataset_path}")
|
| 23 |
+
dataset = droneDataset(dataset_path)
|
| 24 |
+
dynWorld = dataset.dynWorld
|
| 25 |
+
|
| 26 |
+
print("Extracting position data...")
|
| 27 |
+
all_x, all_y = [], []
|
| 28 |
+
|
| 29 |
+
for dynObj in dynWorld.dynamicObjects.values():
|
| 30 |
+
x_valid = [x for x in dynObj.x_vec if not np.isnan(x)]
|
| 31 |
+
y_valid = [y for y in dynObj.y_vec if not np.isnan(y)]
|
| 32 |
+
all_x.extend(x_valid)
|
| 33 |
+
all_y.extend(y_valid)
|
| 34 |
+
|
| 35 |
+
if not all_x:
|
| 36 |
+
print("No position data found!")
|
| 37 |
+
return
|
| 38 |
+
|
| 39 |
+
print(f"Extracted {len(all_x)} data points. Creating heatmap...")
|
| 40 |
+
|
| 41 |
+
plt.figure(figsize=(10, 10))
|
| 42 |
+
plt.style.use("dark_background")
|
| 43 |
+
plt.hist2d(all_x, all_y, bins=(200, 200), cmap="inferno", cmin=1)
|
| 44 |
+
plt.colorbar(label="Traffic density (data points)")
|
| 45 |
+
plt.title("Roundabout Traffic Density Heatmap (Top-View)")
|
| 46 |
+
plt.xlabel("X-Position [m]")
|
| 47 |
+
plt.ylabel("Y-Position [m]")
|
| 48 |
+
plt.gca().set_aspect("equal", adjustable="box")
|
| 49 |
+
plt.tight_layout()
|
| 50 |
+
plt.savefig(output_filename, dpi=300)
|
| 51 |
+
print(f"Heatmap saved: {os.path.abspath(output_filename)}")
|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
if __name__ == "__main__":
|
| 55 |
+
if len(sys.argv) < 2:
|
| 56 |
+
print("Usage: python 02_heatmap_density.py <path_to_recording_folder>")
|
| 57 |
+
else:
|
| 58 |
+
generate_density_heatmap(sys.argv[1])
|
example_scripts/03_high_acceleration.py
ADDED
|
@@ -0,0 +1,61 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
High Acceleration Detection — Automatum Data Roundabout Dataset
|
| 3 |
+
Detects vehicles exceeding a given acceleration threshold.
|
| 4 |
+
|
| 5 |
+
Usage:
|
| 6 |
+
python 03_high_acceleration.py <path_to_recording_folder> [threshold_m_s2]
|
| 7 |
+
|
| 8 |
+
Example:
|
| 9 |
+
python 03_high_acceleration.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d 3.0
|
| 10 |
+
"""
|
| 11 |
+
import sys
|
| 12 |
+
import os
|
| 13 |
+
import numpy as np
|
| 14 |
+
from openautomatumdronedata.dataset import droneDataset
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def detect_high_accelerations(dataset_path, acc_threshold=3.0):
|
| 18 |
+
print(f"Loading dataset from: {dataset_path}")
|
| 19 |
+
dataset = droneDataset(dataset_path)
|
| 20 |
+
dynWorld = dataset.dynWorld
|
| 21 |
+
|
| 22 |
+
print(f"Searching for accelerations > {acc_threshold} m/s^2...")
|
| 23 |
+
|
| 24 |
+
high_accel_vehicles = []
|
| 25 |
+
|
| 26 |
+
for dynObj in dynWorld.dynamicObjects.values():
|
| 27 |
+
length = min(len(dynObj.ax_vec), len(dynObj.ay_vec))
|
| 28 |
+
if length == 0:
|
| 29 |
+
continue
|
| 30 |
+
|
| 31 |
+
ax = np.array(dynObj.ax_vec[:length])
|
| 32 |
+
ay = np.array(dynObj.ay_vec[:length])
|
| 33 |
+
total_accel = np.sqrt(ax**2 + ay**2)
|
| 34 |
+
valid_accel = total_accel[~np.isnan(total_accel)]
|
| 35 |
+
|
| 36 |
+
if len(valid_accel) > 0:
|
| 37 |
+
max_accel = np.max(valid_accel)
|
| 38 |
+
if max_accel > acc_threshold:
|
| 39 |
+
high_accel_vehicles.append({
|
| 40 |
+
"uuid": dynObj.UUID,
|
| 41 |
+
"type": dynObj.type,
|
| 42 |
+
"max_accel": max_accel,
|
| 43 |
+
})
|
| 44 |
+
|
| 45 |
+
if not high_accel_vehicles:
|
| 46 |
+
print(f"No vehicles with acceleration > {acc_threshold} m/s^2 found.")
|
| 47 |
+
return
|
| 48 |
+
|
| 49 |
+
sorted_vehicles = sorted(high_accel_vehicles, key=lambda x: x["max_accel"], reverse=True)
|
| 50 |
+
|
| 51 |
+
print(f"\n--- {len(sorted_vehicles)} vehicles with notable acceleration ---")
|
| 52 |
+
for item in sorted_vehicles:
|
| 53 |
+
print(f"Vehicle {item['uuid']} ({item['type']}): max {item['max_accel']:.2f} m/s^2")
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
if __name__ == "__main__":
|
| 57 |
+
if len(sys.argv) < 2:
|
| 58 |
+
print("Usage: python 03_high_acceleration.py <path_to_recording_folder> [threshold]")
|
| 59 |
+
else:
|
| 60 |
+
threshold = float(sys.argv[2]) if len(sys.argv) >= 3 else 3.0
|
| 61 |
+
detect_high_accelerations(sys.argv[1], threshold)
|
example_scripts/README.md
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Example Scripts — Automatum Data Roundabout Dataset
|
| 2 |
+
|
| 3 |
+
These scripts demonstrate how to work with the Automatum drone traffic data using the `openautomatumdronedata` Python library.
|
| 4 |
+
|
| 5 |
+
## Prerequisites
|
| 6 |
+
|
| 7 |
+
```bash
|
| 8 |
+
pip install openautomatumdronedata numpy matplotlib
|
| 9 |
+
```
|
| 10 |
+
|
| 11 |
+
## Scripts
|
| 12 |
+
|
| 13 |
+
| Script | Description |
|
| 14 |
+
|--------|-------------|
|
| 15 |
+
| `01_lane_changes.py` | Analyzes lane change frequency per vehicle and shows the top 10 |
|
| 16 |
+
| `02_heatmap_density.py` | Creates a traffic density heatmap from all position data |
|
| 17 |
+
| `03_high_acceleration.py` | Detects vehicles exceeding an acceleration threshold |
|
| 18 |
+
|
| 19 |
+
## Usage
|
| 20 |
+
|
| 21 |
+
All scripts take the path to a recording folder as their first argument:
|
| 22 |
+
|
| 23 |
+
```bash
|
| 24 |
+
python 01_lane_changes.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d
|
| 25 |
+
python 02_heatmap_density.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d
|
| 26 |
+
python 03_high_acceleration.py ../Roundabout-St2231-IngolstadtVOC_e63d-e63db143-1e40-4945-8866-464572ebf75d 3.0
|
| 27 |
+
```
|
| 28 |
+
|
| 29 |
+
## Learn More
|
| 30 |
+
|
| 31 |
+
- [Full Documentation](https://openautomatumdronedata.readthedocs.io)
|
| 32 |
+
- [Automatum Data Website](https://automatum-data.com)
|