# UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving

Source: [UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving](<https://www.sota2.com/research/paper/68fa75061e209131cea6557f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:09.928Z

Authors: Daniel Bogdoll, No\\"el Ollick, Tim Joseph, Svetlana Pavlitska, J. Marius Z\\"ollner

Published: 2024-06-10T15:32:16.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.06370>)

[PDF](<https://arxiv.org/pdf/2406.06370.pdf>)