Canadian Adverse Driving Conditions dataset-Reference-Cited by-同舟云学术

Canadian Adverse Driving Conditions dataset

Published:2020-12-27 Issue:4-5 Volume:40 Page:681-690
ISSN:0278-3649
Container-title:The International Journal of Robotics Research
language:en
Short-container-title:The International Journal of Robotics Research

Author:

Pitropov Matthew¹^ORCID,Garcia Danson Evan²^ORCID,Rebello Jason³^ORCID,Smart Michael⁴^ORCID,Wang Carlos⁴^ORCID,Czarnecki Krzysztof¹^ORCID,Waslander Steven³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Waterloo, Canada

2. Department of Electrical and Computer Engineering, University of Toronto, Canada

3. Institute for Aerospace Studies, University of Toronto, Canada

4. University of Waterloo, Canada

Abstract

The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected during winter within the Region of Waterloo, Canada, is the first autonomous driving dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames of annotated data from 8 cameras (Ximea MQ013CG-E2), lidar (VLP-32C), and a GNSS+INS system (Novatel OEM638), collected through a variety of winter weather conditions. The sensors are time synchronized and calibrated with the intrinsic and extrinsic calibrations included in the dataset. Lidar frame annotations that represent ground truth for 3D object detection and tracking have been provided by Scale AI.

Funder

natural sciences and engineering research council of canada

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

Link

http://journals.sagepub.com/doi/pdf/10.1177/0278364920979368

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1. nuScenes: A Multimodal Dataset for Autonomous Driving

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