A Commute in Data: The comma2k19 Dataset
By Harald Schafer, Eder Santana, Andrew Haden, Riccardo Biasini. 2018
95 citations recorded by the source corpus.
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. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma grey panda. Laika, an open-source GNSS processing library, is also introduced here. 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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
- Kazuya Takeda
- Jacob Lambert
- Alexander Carballo
- Ekim Yurtsever
- Maksym Semikin
- Ardi Tampuu
- Tambet Matiisen
- Dmytro Fishman
In Computer Vision and Pattern Recognition
- Ekim Yurtsever
- Alexander Carballo
- Jacob Lambert
- Maksym Semikin
- Kazuya Takeda
- Ardi Tampuu
- Tambet Matiisen
- Dmytro Fishman
Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Cityscapes [270] X X Vehicle Berkley DeepDrive [271] X X Vehicle X X Mapillary [272] X X Vehicle X X Oxford RobotCar [49] X X Vehicle X X X KITTI [174] X X X X Vehicle H3D [273] X X X Vehicle ApolloScape [274] X X X X Vehicle nuScenes [175] X X X X Vehicle X X Udacity [275] X X X X Vehicle DDD17 [85”
- Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, unknown): “ai 2019 [115] 2019 ✓ ✓ ✓ ✓ ✓ 30h San Jose California MIT”
- A Survey of End-to-End Driving: Architectures and Training Methods (2020, background): “ai 2019 [140] 1 X X X X 30h MIT DeepDrive [141] 1 X X X X X X 1100h Berkley DeepDrive-X [142] 1 X X X X X 77h Berkley Oxford RobotCar [143] 4 X X X X X 214h CC BY-NC-SA 4.”