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CADC(Canadian Adverse Driving Conditions Dataset)
Fusion Box Tracking
Autonomous Driving
|...
许可协议: CC-BY-NC 4.0

Overview

High quality data for adverse driving conditions

The CADC dataset aims to promote research to improve self-driving in adverse weather conditions. This is the first public dataset to focus on real world driving data in snowy weather conditions.

It features:

  • 56,000 camera images
  • 7,000 LiDAR sweeps
  • 75 scenes of 50-100 frames each
  • 10 annotation classes
  • Full sensor suite: 1 LiDAR, 8 Cameras, Post-processed GPS/IMU
  • Adverse weather driving conditions, including snow

Data Collection

For this dataset, routes were chosen with various levels of traffic, a variety of vehicles and always with snowfall.

Sequences were selected from data collected within the Region of Waterloo, Canada.

Data annotation

Complex Label Taxonomy

Scale’s data annotation platform combines human work and review with smart tools, statistical confidence checks and machine learning checks to ensure the quality of annotations.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently as measured against seven rigorous quality areas for each annotation.

Citation

Please use the following citation when referencing the dataset:

@article{pitropov2020canadian,
  title={Canadian Adverse Driving Conditions Dataset},
  author={Pitropov, Matthew and Garcia, Danson and Rebello, Jason and Smart, Michael and Wang, Carlos and Czarnecki, Krzysztof and Waslander, Steven},
  journal={arXiv preprint arXiv:2001.10117},
  year={2020}
}
数据概要
数据格式
fusion, point cloud, image,
数据量
56K
文件大小
558.53GB
发布方
University of Waterloo
Waterloo is at the forefront of innovation and is home to transformational research and inspired learning. Located in the heart of Canada's technology hub, we are growing a network of global partnerships that will shape the future by working beyond disciplines and building bridges with industry, institutions and communities.
标注方
Scale AI, Inc
Trusted by world class companies, Scale delivers high quality training data for AI applications such as self-driving cars, mapping, AR/VR, robotics, and more
| 数据量 56K | 大小 558.53GB
CADC(Canadian Adverse Driving Conditions Dataset)
Fusion Box Tracking
Autonomous Driving
许可协议: CC-BY-NC 4.0

Overview

High quality data for adverse driving conditions

The CADC dataset aims to promote research to improve self-driving in adverse weather conditions. This is the first public dataset to focus on real world driving data in snowy weather conditions.

It features:

  • 56,000 camera images
  • 7,000 LiDAR sweeps
  • 75 scenes of 50-100 frames each
  • 10 annotation classes
  • Full sensor suite: 1 LiDAR, 8 Cameras, Post-processed GPS/IMU
  • Adverse weather driving conditions, including snow

Data Collection

For this dataset, routes were chosen with various levels of traffic, a variety of vehicles and always with snowfall.

Sequences were selected from data collected within the Region of Waterloo, Canada.

Data annotation

Complex Label Taxonomy

Scale’s data annotation platform combines human work and review with smart tools, statistical confidence checks and machine learning checks to ensure the quality of annotations.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently as measured against seven rigorous quality areas for each annotation.

Citation

Please use the following citation when referencing the dataset:

@article{pitropov2020canadian,
  title={Canadian Adverse Driving Conditions Dataset},
  author={Pitropov, Matthew and Garcia, Danson and Rebello, Jason and Smart, Michael and Wang, Carlos and Czarnecki, Krzysztof and Waslander, Steven},
  journal={arXiv preprint arXiv:2001.10117},
  year={2020}
}
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