SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-ta...

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Bibliographic Details
Published in2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 21339 - 21350
Main Authors Sun, Tao, Segu, Mattia, Postels, Janis, Wang, Yuxuan, Van Gool, Luc, Schiele, Bernt, Tombari, Federico, Yu, Fisher
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
Adaptation models
Annotations
Data collection
Datasets and evaluation; Motion and tracking; Navigation and autonomous driving; Transfer/low-shot/long-tail learning
Multitasking
Rain
System performance
Uncertainty
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Summary:Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift.
ISSN:2575-7075
DOI:10.1109/CVPR52688.2022.02068