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cs.LG2026
RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning
Steven A. Senczyszyn, Timothy C. Havens, Nathaniel Rice +3
As reinforcement learning (RL) deployments expand into safety-critical domains, existing evaluation methods fail to systematically identify hazards arising from the black-box natur…
cs.LG2024
Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data
Anthony Geglio, Eisa Hedayati, Mark Tascillo +3
This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The results demonstrate the algo…