GCP detects malicious agents in collaborative perception using spatial-temporal aware methods with a confidence-scaled loss and historical BEV flow reconstruction, achieving up to 34.69% AP@0.5 gains under a new BAC attack.
AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.
citing papers explorer
-
GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detection
GCP detects malicious agents in collaborative perception using spatial-temporal aware methods with a confidence-scaled loss and historical BEV flow reconstruction, achieving up to 34.69% AP@0.5 gains under a new BAC attack.
-
From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.