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

• URL: http://arxiv.org/abs/2403.13869v3
• Date: Fri, 5 Apr 2024 15:48:03 GMT
• Title: Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
• Authors: Ruoxuan Bai, Jingxuan Yang, Weiduo Gong, Yi Zhang, Qiujing Lu, Shuo Feng,
• Abstract summary: This study endeavors to develop a criticality prediction model that excels in both precision and recall rates. To validate our approach, we evaluate it in two cases: lunar lander and bipedal walker scenarios.
• Score: 6.229278037668383
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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