metadata
license: cc-by-4.0
tags:
- autonomous-vehicles
- driving
- embeddings
- representation-learning
- computer-vision
- safety
DriveBench-Embeddings: 298,326 Driving Scene Vectors
Author: Nikhil Upadhyay | MSc Business Analytics | Dublin Business School Model: Trazemag/DriveBench
Overview
Pre-computed 256-dimensional DriveBench embeddings for all 298,326 clips from the NVIDIA PhysicalAI-AV dataset across 25 countries.
Use these as features for any downstream driving task without running the model.
File
| File | Size | Shape |
|---|---|---|
drivebench_embeddings.npz |
282 MB | (298326, 256) |
Usage
import numpy as np
from huggingface_hub import hf_hub_download
path = hf_hub_download(
"Trazemag/DriveBench-Embeddings",
"drivebench_embeddings.npz",
repo_type="dataset")
data = np.load(path)
embeddings = data["embeddings"] # (298326, 256) float32
# Match to clip IDs using PRECOG-Labels dataset
# huggingface.co/datasets/Trazemag/PRECOG-Labels
What each embedding captures
Each 256-dim vector encodes:
- Danger context (AUC 0.84 on held-out countries)
- Geographic driving patterns (6 regions, 25 countries)
- Time-of-day risk (peak danger 13:00-15:00)
- Radar sensor health (AUC 1.00)
- Traffic density and scene complexity
Citation
@misc{upadhyay2026drivebench,
title = {DriveBench: General-Purpose Driving Scene Encoder
via Multi-Task Safety-Focused Pre-training across 25 Countries},
author = {Upadhyay, Nikhil},
year = {2026},
url = {https://github.com/TrazeMaG/PRECOG-AV}
}