ad-datasets

Pull requests and forks are still welcome, but I stopped maintaining the project in mid-2023. An even more extensive survey from 02 Jan 2024 is A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook. Another good source is the OpenDriveLab repository, where a dynamic list of datasets from the 06 Dec 2023 survey Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future is hosted

Complete* and curated list of datasets in the field of autonomous driving. More details can be found in ad-datasets: a meta-collection of data sets for autonomous driving. There is also a PDF list of the datasets including citations (where possible).

Contributing

Contributions are very welcome! To add or update a dataset:

• Update my-app/src/data.json:

• Make sure the dataset you add or edit has as many attributes as possible filled out:

  • Some attributes can only be found in associated papers
  • Some attributes can only be found in associated websites
  • Some attributes can only be found in the dataset itself

• Send a pull request based on the created fork

Example Contribution

This is how the KITTI dataset is integrated into the website:

[...]
{
  "id": "KITTI",
  "href": "http://www.cvlibs.net/datasets/kitti/",
  "size_hours": "6",
  "size_storage": "180",
  "frames": "-",
  "numberOfScenes": "50",
  "samplingRate": "10",
  "lengthOfScenes": "-",
  "sensors": "camera, lidar, gps/imu",
  "sensorDetail": "2 greyscale cameras 1.4 MP, 2 color cameras 1.4 MP, 1 lidar 64 beams 360° 10Hz, 1 inertial and GPS navigation system",
  "benchmark": "stereo, optical flow, visual odometry, slam, 3d object detection, 3d object tracking",
  "annotations": "3d bounding boxes",
  "licensing": "Creative Commons Attribution-NonCommercial-ShareAlike 3.0",
  "relatedDatasets": "Semantic KITTI, KITTI-360",
  "publishDate": "2012-03-01",
  "lastUpdate": "2021-02-01",
  "paperTitle": "Vision meets Robotics: The KITTI Dataset",
  "relatedPaper": "http://www.cvlibs.net/publications/Geiger2013IJRR.pdf",
  "location": "Karlsruhe, Germany",
  "rawData": "Yes",
  "DOI": "10.1177/0278364913491297"
},
[...]

* You're missing a dataset? Simply create a pull request ;)

Metadata

In the following, the scheme according to which the entries of the respective properties have resulted is illuminated.

Annotations

This property describes the types of annotations with which the data sets have been provided.

Benchmark

If benchmark challenges are explicitly listed with the data sets, they are specified here.

Citations

Citations states the number of citations of the respective paper of a data set. They are automatically obtained via the Semantic Scholar API based upon either the arxiv link of the related paper or its DOI.

Frames

Frames states the number of frames in the data set. This includes training, test and validation data.

Last Update

If information has been provided on updates and their dates, they can be found in this category.

Licensing

In order to give the users an impression of the licenses of the data sets, information on them is already included in the tool.

Location

This category lists the areas where the data sets have been recorded.

N° Scenes

N° Scenes shows the number of scenes contained in the data set and includes the training, testing and validation segments. In the case of video recordings, one recording corresponds to one scene. For data sets consisting of photos, a photo is the equivalent to a scene.

Publish Date

The initial publication date of the data set can be found under this category. If no explicit information on the date of publication of the data set could be found, the submission date of the paper related to the set was used at this point.

Related Data Sets

If data sets are related, the names of the related sets can be examined as well. Related data sets are, for example, those published by the same authors and building on one another.

Related Paper

This property solely consists of a link to the paper related to the data set. Please use an arXiv URL, whenever available.

Sampling Rate [Hz]

The Sampling Rate [Hz] property specifies the sampling rate in Hertz at which the sensors in the data set work. However, this declaration is only made if all sensors are working at the same rate or, alternatively, if the sensors are being synchronized. Otherwise the field remains empty.

Scene Length [s]

This property describes the length of the scenes in seconds in the data set, provided all scenes have the same length. Otherwise no information is given. For example, if a data set has scenes with lengths between 30 and 60 seconds, no entry can be made. The background to this procedure is to maintain comparability and sortability.

Sensor Types

This category contains a rough description of the sensor types used. Sensor types are, for example, lidar or radar.

Sensors - Details

The Sensors - Detail category is an extension of the Sensor Types category. It includes a more detailed description of the sensors. The sensors are described in detail in terms of type and number, the frame rates they work with, the resolutions which sensors have and the horizontal field of view.

Size [GB]

The category Size [GB] describes the storage size of the data set in gigabytes.

Size [h]

The Size [h] property is the equivalent of the Size [GB] described above, but provides information on the size of the data set in hours.

rawData

Denotes if the dataset provides raw or processed data

DOI

DOI of the paper

Citation

If you find our work useful for your research, please cite our paper:

@inProceedings{Bogdoll_Addatasets_2022_VEHITS,
    author    = {Bogdoll, Daniel and Schreyer, Felix and Z\"{o}llner, J. Marius},
    title     = {{ad-datasets: a meta-collection of data sets for autonomous driving}},
    booktitle   = {International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)},
    year      = {2022}
}