Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted to a closed set of semantic classes available in their training data, and are therefore unreliable when confronted with previously unseen instances. Thus, multiple perception datasets have been created for the evaluation of anomaly detection methods, which can be categorized into three groups: real anomalies in real-world, synthetic anomalies augmented into real-world and completely synthetic scenes. This survey provides a structured and, to the best of our knowledge, complete overview and comparison of perception datasets for anomaly detection in autonomous driving. Each chapter provides information about tasks and ground truth, context information, and licenses. Additionally, we discuss current weaknesses and gaps in existing datasets to underline the importance of developing further data.

翻译：深度神经网络（DNN）在自动驾驶感知系统中需要大量数据进行训练，因为必须在各类场景下可靠地实现高性能。然而，这些DNN通常局限于训练数据中封闭的语义类别集合，在面对先前未见的实例时可靠性不足。为此，研究者创建了多个用于异常检测方法评估的感知数据集，可归为三类：真实世界中的真实异常、增强至真实世界的合成异常以及完全合成场景。本综述首次（据我们所知）以结构化方式提供自动驾驶异常检测感知数据集的完整概述与比较。每章涵盖任务与地面真值、上下文信息及许可证内容。此外，我们讨论了现有数据集存在的缺陷与不足，以强调进一步开发数据的必要性。