Plug-and-Play Reweighting Improves Resilience in Collaborative Autonomous Driving

Jul 14, 2026

A new Resilient Collaborative Decision-Making (RCDM) framework for connected autonomous vehicles introduces a plug-and-play reweighting module that down-weights corrupted inputs without requiring retraining. The approach leverages attention-based encoders and decoders to process and fuse perceptions from multiple vehicles, and the reweighting module assigns lower weights to inconsistent or potentially corrupted data. In high-fidelity simulations, the method outperformed existing approaches by up to 26% under various types of perceptual noise and adversarial attacks.

Why it matters: This work offers a practical, retraining-free method to enhance the robustness of collaborative autonomous vehicle systems against corrupted sensor data and attacks.

Full story at: arXiv Robotics ↗