Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). This work introduces the Risk Ranked Recall ($R^3$) metrics for object detection systems. The $R^3$ metrics categorize objects within three ranks. Ranks are assigned based on an objective cyber-physical model for the risk of collision. Recall is measured for each rank.

翻译：用于评价物体探测系统(精密、回顾、MAP)的通用衡量标准没有提供完整资料,说明其是否适合用于安全关键任务,如在机动车辆(AV)中为避免碰撞而探测障碍,这项工作为物体探测系统引入了按风险排序召回(R%3美元)的衡量标准,用3美元的衡量标准将物体分为三类,根据客观的网络物理模型分配等级,以衡量碰撞风险,对每一等级进行衡量。