CADC
3D 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

Instances Per Label

img

Data Collection

Complex Driving Scenarios in Adverse Conditions

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.

Vehicle, Sensor and Camera Details

We collected data using the Autonomoose, a Lincoln MKZ Hybrid mounted with a full suite of LiDAR, inertial and vision sensors.

Please refer to the figure below for the sensor configuration of the Autonomoose.

Wide Angle Cameras

  • 10 Hz capture frequency
  • 1/1.8” CMOS sensor of 1280x1024 resolution
  • Images are stored as PNG

LiDAR

  • 10 Hz capture frequency
  • 32 channels
  • 200m range
  • 360° horizontal FOV; 40° vertical FOV (-25° to +15°)

Post-processed GPS and IMU

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}
}

License

CC BY-NC 4.0

数据概要
数据格式
Point Cloud, Image,
数据量
56K
文件大小
543.7GB
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
数据集反馈
| 88 | 数据量 56K | 大小 543.7GB
CADC
3D 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

Instances Per Label

img

Data Collection

Complex Driving Scenarios in Adverse Conditions

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.

Vehicle, Sensor and Camera Details

We collected data using the Autonomoose, a Lincoln MKZ Hybrid mounted with a full suite of LiDAR, inertial and vision sensors.

Please refer to the figure below for the sensor configuration of the Autonomoose.

Wide Angle Cameras

  • 10 Hz capture frequency
  • 1/1.8” CMOS sensor of 1280x1024 resolution
  • Images are stored as PNG

LiDAR

  • 10 Hz capture frequency
  • 32 channels
  • 200m range
  • 360° horizontal FOV; 40° vertical FOV (-25° to +15°)

Post-processed GPS and IMU

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}
}

License

CC BY-NC 4.0

数据集反馈
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