Plug-and-Play Reweighting for Resilient Collaborative Decision-Making in Connected Autonomous Driving

arXiv:2607.10037v1 Announce Type: new Abstract: Collaborative decision-making is a fundamental capability in multi-robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collaborators can severely affect decision reliability. Overall, existing methods typically rely on retraining with attack-specific defenses or on restrictive perturbation assumptions to improve resilience, which limits their practicality. In this paper, we propose a novel Resi...