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.

本研究利用生成世界模型和无监督图像分割，重新审视无监督异常检测，并提出 UMAD 方法，其在无监督异常检测方面表现优于现有技术。

UMAD：无监督自动驾驶遮罩层异常检测

UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving

End-to-end learning has emerged as a major paradigm for developing autonomous
systems. Unfortunately, with its performance and convenience comes an even
greater challenge of safety assurance. A key factor of this challenge is the
absence of the notion of a low-dimensional and interpretable dynamical state,
around which traditional assurance methods revolve. Focusing on the online
safety prediction problem, this paper proposes a configurable family of
learning pipelines based on generative world models, which do not require
low-dimensional states. To implement these pipelines, we overcome the
challenges of learning safety-informed latent representations and missing
safety labels under prediction-induced distribution shift. These pipelines come
with statistical calibration guarantees on their safety chance predictions
based on conformal prediction. We perform an extensive evaluation of the
proposed learning pipelines on two case studies of image-controlled systems: a
racing car and a cartpole.

通过基于生成世界模型的可配置学习流水线，在不需要低维状态的情况下解决了在线安全预测问题，并提供了统计校准的安全机会预测保证。通过对图像控制系统的两个案例研究（赛车和平衡杆）进行广泛评估。