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
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}
}