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README.md

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+---
+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}
+}
+```