license: mit
dataset_info:
features:
- name: segment_id
dtype: string
- name: preview
dtype: image
- name: log
struct:
- name: global_pose__frame_gps_times
list:
list: float64
- name: global_pose__frame_orientations
list:
list: float64
- name: global_pose__frame_positions
list:
list: float64
- name: global_pose__frame_times
list: float64
- name: global_pose__frame_velocities
list:
list: float64
- name: processed_log__CAN__radar__t
list: float64
- name: processed_log__CAN__radar__value
list:
list: float64
- name: processed_log__CAN__raw_can__address
list: int64
- name: processed_log__CAN__raw_can__data
list: binary
- name: processed_log__CAN__raw_can__src
list: int64
- name: processed_log__CAN__raw_can__t
list: float64
- name: processed_log__CAN__speed__t
list: float64
- name: processed_log__CAN__speed__value
list:
list: float64
- name: processed_log__CAN__steering_angle__t
list: float64
- name: processed_log__CAN__steering_angle__value
list: float64
- name: processed_log__CAN__wheel_speed__t
list: float64
- 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
list: float64
- name: processed_log__IMU__accelerometer__value
list:
list: float64
- name: processed_log__IMU__gyro__t
list: float64
- name: processed_log__IMU__gyro__value
list:
list: float64
- 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: 537452565
num_examples: 64
download_size: 227930673
dataset_size: 537452565
configs:
- config_name: default
data_files:
- split: demo
path: data/demo-*
comma2k19
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 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.
Here we also introduced 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.
Publication
For a detailed write-up about this dataset, please refer to our paper. If you use comma2k19 or Laika in your research, please consider citing
@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},
}