Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems

arXiv:2403.13869v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenge cs.ai cs.lg current daily deployment events integral intelligent intelligent systems practical probability safety safety-critical state systems threats type