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.
The software-defined vehicle: A com- prehensive study on current trends and challenges
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A multilayer offloading pipeline for dynamic vehicular networks uses modified PSO to cut average response times versus brute force while meeting latency constraints in simulated edge setups.
citing papers explorer
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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.
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Towards Intelligent Computation Offloading in Dynamic Vehicular Networks: A Scalable Multilayer Pipeline
A multilayer offloading pipeline for dynamic vehicular networks uses modified PSO to cut average response times versus brute force while meeting latency constraints in simulated edge setups.