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.Comment: Accepted for publication at IV 202