Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
<|reference_start|>Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems: 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.<|reference_end|>
@article{bai2024accurately,
    title={Accurately Predicting Probabilities of Safety-Critical Rare Events for
  Intelligent Systems},
    author={Ruoxuan Bai, Jingxuan Yang, Weiduo Gong, Yi Zhang, Qiujing Lu and Shuo
  Feng},
    journal={arXiv preprint arXiv:2403.13869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13869},
    primaryClass={cs.LG cs.AI}
}