In autonomous driving, the most challenging scenarios are the ones that can only be detected within their temporal context. Most video anomaly detection approaches focus either on surveillance or traffic accidents, which are only a subfield of autonomous driving. In this work, we present HF$^2$-VAD$_{AD}$, a variation of the HF$^2$-VAD surveillance video anomaly detection method for autonomous driving. We learn a representation of normality from a vehicle's ego perspective and evaluate pixel-wise anomaly detections in rare and critical scenarios.

翻译：在自动驾驶中，最具挑战性的场景是那些仅能在其时间上下文中被检测到的情况。大多数视频异常检测方法要么专注于监控，要么专注于交通事故，而这些只是自动驾驶的一个子领域。在本研究中，我们提出了HF$^2$-VAD$_{AD}$，这是HF$^2$-VAD监控视频异常检测方法针对自动驾驶场景的一个变体。我们从车辆的自我视角学习正常状态的表示，并在罕见且关键的情景中评估像素级的异常检测。