  Dealing with atypical traffic scenarios remains a challenging task in
autonomous driving. However, most anomaly detection approaches cannot be
trained on raw sensor data but require exposure to outlier data and powerful
semantic segmentation models trained in a supervised fashion. This limits the
representation of normality to labeled data, which does not scale well. In this
work, we revisit unsupervised anomaly detection and present UMAD, leveraging
generative world models and unsupervised image segmentation. Our method
outperforms state-of-the-art unsupervised anomaly detection.
