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重大潜在威胁。解决这一挑战的关键在于准确预测在当前状态下的一段时间步长内发生安全关键事件的概率，我们将这一指标定义为“临界性”。预测临界性的复杂性源于罕见事件导致的高维变量中出现极端数据不平衡，这一挑战我们称之为“罕见性诅咒”。现有方法往往过于保守或容易忽略安全关键事件，因此难以同时实现高精确率和高召回率，这严重限制了它们的适用性。本研究致力于开发一种在精确率和召回率方面均表现优异的临界性预测模型，用于评估安全关键自主系统的临界性。我们提出了一种多阶段学习框架，旨在逐步密化数据集，跨阶段缓解罕见性诅咒。为验证我们的方法，我们在两种场景下进行了评估：月球着陆器和双足步行器。结果表明，我们的方法优于传统方法，能够更准确、更可靠地评估智能系统的临界性。