| --- |
| 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. |
|
|
|  |
|
|
| 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() |
| ``` |
|
|
|  |
|
|
| ```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() |
| ``` |
|
|
|  |
|
|
|
|
