In autonomous driving, the most challenging scenarios 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. 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，最具挑战性的场景仅能在其时间上下文中被检测到。大多数视频异常检测方法要么专注于监控场景，要么专注于交通事故，而这些仅是自动驾驶的子领域。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，并在罕见且关键的场景中评估像素级的异常检测。