SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
Deep reinforcement learning-enabled computation offloading: A novel framework to energy optimization and security- aware in vehicular edge-cloud computing networks
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SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions
SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.