| --- |
| 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](https://huggingface.co/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 |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
