  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.
