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README.md

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