metadata
license: other
license_link: LICENSE.md
license_name: comma-av-dataset
short_description: >-
A large self-driving dataset containing one-minute driving segments with
road-camera video, and full localization data.
configs:
- config_name: thumbnails
default: true
data_files:
- split: test
path: data/*/thumbnail.jpg
drop_labels: true
comma 5k
A 5k preview of comma.ai's self-driving dataset containing one-minute driving segments with road-camera video, logs, and full localization data.
Dataset collection
Segments were recorded by comma devices installed in real user vehicles. The collection spans several hardware generations (comma three, comma 3X, and comma four)
Each segment includes a narrow FOV camera, wide FOV camera, preview thumbnail, log file with various measurements (GNSS, IMU, etc.), frame timing data, and offline localization data.
The offline localizer.safetensors is the result of an a fusion of raw GNSS measurements, accelerometer and gyroscope data, vehicle motion constraints, and visual feature tracks from the road cameras providing an accurate localization estimate.
Structure
data/
└── <segment_id>/
├── fcamera.hevc
├── ecamera.hevc
├── thumbnail.jpg
├── localizer.safetensors
├── frame_info.safetensors
└── rlog.zst
Example usage
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/comma5k", repo_type="dataset",
filename=f"data/{segment_id}/thumbnail.jpg",
)
Image.open(thumbnail_path).show()
localizer_path = hf_hub_download(
repo_id="commaai/comma5k", 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()
from openpilot.tools.lib.logreader import LogReader
rlog_path = hf_hub_download(
repo_id="commaai/comma5k",
repo_type="dataset",
filename=f"data/{segment_id}/rlog.zst",
)
lr = LogReader(rlog_path)
speeds = [state.vEgo for state in lr.filter("carState")]
print(f"max speed: {max(speeds):.2f} m/s")
# max speed: 16.76 m/s