Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this challenge hinges on accurately predicting the probability of safety-critical events occurring within a given time step from the current state, a metric we define as 'criticality'. The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity. Existing methods tend to be either overly conservative or prone to overlooking safety-critical events, thus struggling to achieve both high precision and recall rates, which severely limits their applicability. This study endeavors to develop a criticality prediction model that excels in both precision and recall rates for evaluating the criticality of safety-critical autonomous systems. We propose a multi-stage learning framework designed to progressively densify the dataset, mitigating the curse of rarity across stages. To validate our approach, we evaluate it in two cases: lunar lander and bipedal walker scenarios. The results demonstrate that our method surpasses traditional approaches, providing a more accurate and dependable assessment of criticality in intelligent systems.

翻译：智能系统在我们的日常生活中日益不可或缺，但罕见的安全关键事件对其实际部署构成了显著潜在威胁。应对这一挑战的关键在于准确预测从当前状态出发、在给定时间步内发生安全关键事件的概率，我们将这一指标定义为“关键性”。预测关键性的复杂性源于罕见事件所引发的高维变量数据极端不平衡，我们将这一挑战称为“稀少性诅咒”。现有方法往往过于保守或容易忽视安全关键事件，因此难以同时实现高精确率与高召回率，这严重限制了它们的适用性。本研究致力于开发一种在评估安全关键自主系统关键性方面兼具高精确率与高召回率的预测模型。我们提出了一种多阶段学习框架，旨在逐步稠密化数据集，从而在各个阶段缓解“稀少性诅咒”。为验证我们方法的有效性，我们在月球着陆器和双足步行器两个场景中进行了评估。结果表明，我们的方法超越了传统方法，为智能系统的关键性评估提供了更准确、更可靠的依据。