Nowadays, supervised deep learning techniques yield the best state-of-the-art
prediction performances for a wide variety of computer vision tasks. However,
such supervised techniques generally require a large amount of manually labeled
training data. In the context of autonomous vehicles perception, this
requirement is critical, as the distribution of sensor data can continuously
change and include several unexpected variations. It turns out that a category
of learning techniques, referred to as self-supervised learning (SSL), consists
of replacing the manual labeling effort by an automatic labeling process.
Thanks to their ability to learn on the application time and in varying
environments, state-of-the-art SSL techniques provide a valid alternative to
supervised learning for a variety of different tasks, including long-range
traversable area segmentation, moving obstacle instance segmentation, long-term
moving obstacle tracking, or depth map prediction. In this tutorial-style
article, we present an overview and a general formalization of the concept of
self-supervised learning (SSL) for autonomous vehicles perception. This
formalization provides helpful guidelines for developing novel frameworks based
on generic SSL principles. Moreover, it enables to point out significant
challenges in the design of future SSL systems.

本文介绍了自监督学习技术在自动驾驶感知中的应用，其能够替代传统手动标注的方式，为自动驾驶中的不同任务提供了有效的替代方法，并提出了未来自监督学习系统设计中的重要挑战。