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，以确保其在各种场景下都能可靠地实现高性能。然而，这些DNN通常局限于训练数据中预设的封闭语义类别集合，因此面对前所未见的实例时可靠性不足。为此，研究人员构建了多个用于评估异常检测方法的感知数据集，这些数据集可分为三类：真实世界中的真实异常、增强到真实场景中的合成异常，以及完全合成的场景。本综述首次（据我们所知）系统性地梳理并对比了自动驾驶异常检测领域的感知数据集。每章节均提供任务定义与真实标注、上下文信息及许可证信息。此外，我们讨论了现有数据集的缺陷与空白，以强调持续开发新数据的重要性。