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.

翻译：处理非典型交通场景仍是自动驾驶领域的一项挑战性任务。然而，大多数异常检测方法无法基于原始传感器数据进行训练，而需要接触离群数据以及通过监督方式训练的强语义分割模型。这导致正常性表征受限于标注数据，其扩展性较差。本研究重新审视无监督异常检测问题，提出UMAD方法，该方法利用生成式世界模型与无监督图像分割技术。我们的方法在性能上超越了当前最先进的无监督异常检测方法。