AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving

View a PDF of the paper titled AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving, by Daniel Bogdoll and Iramm Hamdard and Lukas Namgyu R”o{ss}ler and Felix Geisler and Muhammed Bayram and Felix Wang and Jan Imhof and Miguel de Campos and Anushervon Tabarov and Yitian Yang and Hanno Gottschalk and J. Marius Z”ollner

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Abstract:The scale-up of autonomous vehicles depends heavily on their ability to deal with anomalies, such as rare objects on the road. In order to handle such situations, it is necessary to detect anomalies in the first place. Anomaly detection for autonomous driving has made great progress in the past years but suffers from poorly designed benchmarks with a strong focus on camera data. In this work, we propose AnoVox, the largest benchmark for ANOmaly detection in autonomous driving to date. AnoVox incorporates large-scale multimodal sensor data and spatial VOXel ground truth, allowing for the comparison of methods independent of their used sensor. We propose a formal definition of normality and provide a compliant training dataset. AnoVox is the first benchmark to contain both content and temporal anomalies.