论文标题
Shift：用于连续多任务域适应的合成驾驶数据集
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
论文作者
论文摘要
适应不断发展的环境是所有自动驾驶系统不可避免地面临的安全挑战。但是，现有的图像和视频驱动数据集却没有捕获现实世界的可变性质。在本文中，我们介绍了最大的多任务合成数据集，用于自动驾驶，转移。它表现出云彩，雨水和雾气，一天中的时间以及车辆和行人密度的离散和连续变化。 Shift采用了几个主流感知任务的全面传感器套件和注释，可以调查在域转移水平上增加感知系统性能的降解，从而促进持续适应策略的发展，以减轻此问题并评估模型的鲁棒性和一般性。我们的数据集和基准工具包可在www.vis.xyz/shift上公开获得。
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems. Existing image and video 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 investigating the degradation of a perception system performance at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assess model robustness and generality. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift.
