  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.
