| --- |
| license: cc-by-4.0 |
| tags: |
| - autonomous-vehicles |
| - danger-detection |
| - driving |
| - safety |
| task_categories: |
| - object-detection |
| language: |
| - en |
| --- |
| |
| # PRECOG-Labels: PhysicalAI-AV Danger Labels |
|
|
| **Author:** Nikhil Upadhyay | MSc Business Analytics | Dublin Business School |
| **Project:** [PRECOG-AV](https://github.com/TrazeMaG/PRECOG-AV) |
|
|
| ## Overview |
|
|
| Physics-based danger labels for **298,326 clips** from the |
| [NVIDIA PhysicalAI-AV](https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles) dataset, |
| mined using Time-to-Collision (TTC) analysis of 3D obstacle parquets. |
| The first danger labelling at this geographic scale: **25 countries**. |
|
|
| **14,192 danger clips identified (4.76% positive rate)** |
|
|
| ## Files |
|
|
| | File | Rows | Description | |
| |------|------|-------------| |
| | `danger_labels.csv` | 298,326 | Binary danger flag and min TTC per clip | |
| | `precog_dataset.csv` | 298,326 | Full metadata: country, split, hour, sensor availability | |
|
|
| ## Geographic Split |
|
|
| | Split | Countries | Clips | |
| |-------|-----------|-------| |
| | Train | 20 countries | 275,573 | |
| | Val | Austria, Finland, Portugal | 16,151 | |
| | Test | Greece, Bulgaria | 6,602 | |
|
|
| ## Key Findings |
|
|
| - Danger rates vary **4.7x across countries** — Italy 9.9% vs Estonia 2.1% |
| - Peak danger window: **13:00 to 15:00** across all countries |
| - Mean danger window: **11.18 seconds** across 2,177 labelled test clips |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{upadhyay2026precog, |
| title = {PRECOG: Proactive Risk and Environmental Cognition for Autonomous Vehicles}, |
| author = {Upadhyay, Nikhil}, |
| year = {2026}, |
| url = {https://github.com/TrazeMaG/PRECOG-AV} |
| } |
| ``` |
