README.md

metadata

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

Overview

Physics-based danger labels for 298,326 clips from the NVIDIA PhysicalAI-AV 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

@misc{upadhyay2026precog,
  title  = {PRECOG: Proactive Risk and Environmental Cognition for Autonomous Vehicles},
  author = {Upadhyay, Nikhil},
  year   = {2026},
  url    = {https://github.com/TrazeMaG/PRECOG-AV}
}