README.md

---
license: mit
dataset_info:
  features:
  - name: segment_id
    dtype: string
  - name: video_preview
    dtype: image
  - name: preview
    dtype: image
  - name: log
    struct:
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    - name: processed_log__CAN__radar__value
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    - name: processed_log__CAN__steering_angle__value
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    - name: processed_log__CAN__wheel_speed__t
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    - name: processed_log__CAN__wheel_speed__value
      list:
        list: float64
    - name: processed_log__GNSS__live_gnss_qcom__t
      list: float64
    - name: processed_log__GNSS__live_gnss_qcom__value
      list:
        list: float64
    - name: processed_log__GNSS__live_gnss_ublox__t
      list: float64
    - name: processed_log__GNSS__live_gnss_ublox__value
      list:
        list: float64
    - name: processed_log__GNSS__raw_gnss_qcom__t
      list: float64
    - name: processed_log__GNSS__raw_gnss_qcom__value
      list:
        list: float64
    - name: processed_log__GNSS__raw_gnss_ublox__t
      list: float64
    - name: processed_log__GNSS__raw_gnss_ublox__value
      list:
        list: float64
    - name: processed_log__IMU__accelerometer__t
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    - name: processed_log__IMU__accelerometer__value
      list:
        list: float64
    - name: processed_log__IMU__gyro__t
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    - name: processed_log__IMU__gyro__value
      list:
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    - name: processed_log__IMU__gyro_bias__t
      list: float64
    - name: processed_log__IMU__gyro_bias__value
      list:
        list: float64
    - name: processed_log__IMU__gyro_uncalibrated__t
      list: float64
    - name: processed_log__IMU__gyro_uncalibrated__value
      list:
        list: float64
    - name: processed_log__IMU__magnetometer__t
      list: float64
    - name: processed_log__IMU__magnetometer__value
      list:
        list: float64
    - name: processed_log__IMU__magnetometer_uncalibrated__t
      list: float64
    - name: processed_log__IMU__magnetometer_uncalibrated__value
      list:
        list: float64
  splits:
  - name: demo
    num_bytes: 2067369087
    num_examples: 64
  download_size: 1761007824
  dataset_size: 2067369087
configs:
- config_name: default
  data_files:
  - split: demo
    path: data/demo-*
---

# comma2k19
[comma.ai](https://comma.ai) presents comma2k19, a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. comma2k19 is a fully reproducible and scalable dataset. The data was collected using comma [EONs](https://comma.ai/shop/products/eon-gold-dashcam-devkit/) that has sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma [grey panda](https://comma.ai/shop/products/panda-obd-ii-dongle/). 

<img src="https://github.com/commaai/comma2k19/blob/master/assets/testmesh3d.png?raw=true"/>

Here we also introduced [Laika](https://github.com/commaai/laika), an open-source GNSS processing library. Laika produces 40% more accurate positions than the GNSS module used to collect the raw data. This dataset includes pose (position + orientation) estimates in a global reference frame of the recording camera. These poses were computed with a tightly coupled INS/GNSS/Vision optimizer that relies on data processed by Laika. comma2k19 is ideal for development and validation of tightly coupled GNSS algorithms and mapping algorithms that work with commodity sensors. 

<img src="https://github.com/commaai/comma2k19/blob/master/assets/merged.png?raw=true"/>

## Publication
For a detailed write-up about this dataset, please refer to our [paper](https://arxiv.org/abs/1812.05752v1). If you use comma2k19 or Laika in your research, please consider citing
```text
@misc{1812.05752,
Author = {Harald Schafer and Eder Santana and Andrew Haden and Riccardo Biasini},
Title = {A Commute in Data: The comma2k19 Dataset},
Year = {2018},
Eprint = {arXiv:1812.05752},
}
```