---
license: other
license_link: LICENSE.md
license_name: comma-av-dataset
configs:
- config_name: thumbnails
  default: true
  data_files:
  - split: test
    path: "data/*/thumbnail.jpg"
  drop_labels: true
---
# comma 1M

<video title="source" controls>
  <source src="https://cdn-uploads.huggingface.co/production/uploads/608c79739c5a8f8ddd85c409/SVwh26SkRYi5QsH6A76xj.mp4" type="video/mp4">
</video>

A large self-driving dataset containing one-minute driving segments with road-camera video, and full localization data.

## Dataset collection

Segments were recorded by comma devices installed in real user vehicles. The collection spans several hardware generations (comma two, comma three, comma 3X, and comma four)

Camera systems vary between generations, so native resolution and field of view are not uniform across the dataset.

![hw_types](https://cdn-uploads.huggingface.co/production/uploads/608c79739c5a8f8ddd85c409/Ef4v5j12CW1AHGgzqvMGp.png)

Each segment includes an offline localization estimate in `localizer.safetensors`. It fuses raw GNSS measurements, accelerometer and gyroscope data, vehicle motion constraints, and visual feature tracks from the road cameras.


## Structure

```text
data/
└── <segment_id>/
    ├── fcamera.hevc
    ├── ecamera.hevc (for segments collected using hardware type >= comma three)
    ├── thumbnail.jpg
    └── localizer.safetensors
```

## Example usage

```python
import numpy as np
import plotly.graph_objects as go
from huggingface_hub import hf_hub_download
from safetensors.numpy import load_file
from pymap3d import ecef2geodetic
from PIL import Image
segment_id = "001774ef60cf6c43657cd317035fae58"

thumbnail_path = hf_hub_download(
  repo_id="commaai/comma1M", repo_type="dataset",
  filename=f"data/{segment_id}/thumbnail.jpg",
)
Image.open(thumbnail_path).show()
```

![tmpwp75ks43](https://cdn-uploads.huggingface.co/production/uploads/608c79739c5a8f8ddd85c409/c6DkyVrD6644hwFgt1Aow.png)

```python
localizer_path = hf_hub_download(
  repo_id="commaai/comma1M", repo_type="dataset",
  filename=f"data/{segment_id}/localizer.safetensors",
)
states = load_file(localizer_path)["states"]
latitude, longitude, _ = ecef2geodetic(*states[:, :3].T)
speed = np.linalg.norm(states[:, 7:10], axis=1)
fig = go.Figure()
fig.add_trace(go.Scattermap(lat=latitude[::10], lon=longitude[::10],
  mode="lines+markers", line={"color": "#ff4d4d", "width": 3},
  marker={"size": 6,"color": speed[::10],"colorscale": "Turbo","colorbar": {"title": "speed (m/s)"}}))
# span and zoom for map
span = max(float(np.ptp(latitude)), float(np.ptp(longitude)), 1e-6)
zoom = float(np.clip(np.log2(360.0 / span) - 1., 1, 18))
fig.update_layout(map={"style": "open-street-map","center": {"lat": float(np.mean(latitude)), "lon": float(np.mean(longitude))},"zoom": zoom},
  legend={"orientation": "h", "x": 0.01, "y": 0.01},
)
fig.show()
```

![newplot](https://cdn-uploads.huggingface.co/production/uploads/608c79739c5a8f8ddd85c409/MR00BzcF_gCATP4nombod.png)