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Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems
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.
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
A common approach involves designing testing scenarios based on prior knowledge of CAVs (e. g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances.
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