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Jun, 2024
UMAD:无监督自动驾驶遮罩层异常检测
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving
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Daniel Bogdoll, Noël Ollick, Tim Joseph, J. Marius Zöllner
TL;DR
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。
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 p
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