Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite this progress and tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to new, unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. The task, which these methods aim to solve is termed unsupervised domain adaptation (UDA). While several different domain shifts can challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. In this work, we present an overview of the current state of the art in this field of research. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. The scope of this survey goes far beyond the description of the UDA state-of-the-art. Based on our large data and knowledge base, we present a quantitative comparison of the approaches and use the observations to point out the latest trends in this field. In the following, we conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions to generalize DNNs better.

翻译：深度神经网络（DNN）近年来在机器人、自动驾驶等众多领域展现了卓越能力，推动了技术突破。在极具挑战性的自动驾驶应用中，DNN在环境感知方面扮演重要角色，被用于目标检测、语义分割及传感器融合等任务。尽管取得了这些进展和大量研究工作，但仍有若干问题亟待解决，限制了DNN在自动驾驶中的适用性。DNN对未见新域的泛化能力不足是实现安全大规模应用的主要障碍，因为新域的手动标注成本高昂，尤其是对于语义分割而言。因此，需要无需标注即可将DNN适应至新域的方法。这类方法旨在解决的问题被称为无监督域适应（UDA）。虽然多种域偏移可能挑战DNN，但合成数据与真实数据之间的偏移对自动驾驶尤为重要，因为这允许利用仿真环境进行DNN训练。本文对该研究领域的当前最新进展进行了概述。我们对不同的UDA方法进行了分类与阐释。本文涵盖的文献数量超过该主题的任何其他综述。本综述的范围远超对UDA现状的描述。基于我们庞大的数据与知识库，我们提供了方法的定量比较，并利用观察结果指出了该领域的最新趋势。随后，我们对现有技术进行了批判性分析，并指出了有前景的未来研究方向。通过本综述，我们旨在进一步推动UDA研究，并鼓励科学家探索新颖的研究方向，以更好地提升DNN的泛化能力。