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

• Published in: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) pp. 3243 - 3249
• Main Authors: Bai, Ruoxuan, Yang, Jingxuan, Gong, Weiduo, Zhang, Yi, Lu, Qiujing, Feng, Shuo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28-08-2024
• Subjects: Autonomous systems; Complexity theory; Computer aided software engineering; Fasteners; Measurement; Moon; Predictive models; Reinforcement learning; Space vehicles; Unsupervised learning
• ISSN: 2161-8089
• DOI: 10.1109/CASE59546.2024.10711532

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 multistage 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.