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

July 10, 2026 ยท Grace Period ยท ๐Ÿ› the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems

โณ Grace Period
This paper is less than 90 days old. We give authors time to release their code before passing judgment.
Authors Jiewen Liu, Rui Liu, Matthew Lee, Ming C. Lin, Xiaorui Liu, Peng Gao arXiv ID 2607.10037 Category cs.RO: Robotics Citations 0 Venue the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems
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 Resilient Collaborative Decision-Making (RCDM) framework that consists of an attention-based encoder for extracting individual robot perceptual embeddings and an attention-based decoder for fusing collaborator perceptions and making decisions. To improve resilience to corrupted observations, we design a novel plug-and-play reweighting module that down-weights the influence of corrupted inputs by analyzing the consistency of neighborhood points relative to the local structure and assigning smaller weights to points that deviate strongly from the local median. This module can be seamlessly integrated into attention-based collaborative decision-making without requiring additional training. We evaluate our method in high-fidelity simulations, considering perceptual noise and five types of attacks across diverse accident-prone scenarios. Experimental results demonstrate that our approach consistently outperforms existing methods by up to 26% and achieves state-of-the-art resilient performance.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Robotics