Trust-Aware Cognitive Graphs for Human–Machine Coordination in Multi-Agent Autonomous Driving

Connected urban driving increasingly involves mixed teams of autonomous vehicles and humans that must coordinate under partial observability, non-stationarity, and perception failures. Many multi-agent learning pipelines implicitly assume reliable teammate signals, leaving coordination brittle when messages drop, observations degrade, or a participant behaves inconsistently. We propose CG-HMT (Cognitive Graphs for Human–Machine Teaming), a framework that makes team cognition explicit for both coordination and learning. CG-HMT represents all participants as nodes in a fully directed, time-varying trust graph; each agent maintains a compact mental model (belief, evidential intent with uncertainty, peer expectation), and trust-weighted fusion produces per-node shared mental models. Cognitive divergence (belief gaps, uncertainty spikes, expectation mismatches) triggers low-burden operator prompts mapped to bounded trust nudges. We prove a linear bound on the influence of a Byzantine trustee in trust-weighted fusion and derive a weighted policy-gradient interpretation of trust- and uncertainty-modulated learning. In CARLA-based multi-agent driving, CG-HMT achieves the strongest clean-condition coordination (75% vs. 20% episode-level success for the best baseline, three seeds) and shows a safety–completion trade-off under V2X dropouts, adverse weather, and a Byzantine teammate, with lower observed violation counts in several shifted settings but sometimes lower task completion.