Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey

Abstract

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.

arXiv commentAccepted for publication at IV 2023

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Cited by in corpus (4)

• AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving
• Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
• Conditioning Latent-Space Clusters for Real-World Anomaly Classification
• Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation