LR-V2X: Loss-Resilient Collaborative Perception under Low-Bandwidth Communication

2026-10-08

AI Summary

This study addresses the challenges of unreliable communication and irrecoverable feature loss in V2X cooperative perception under low-bandwidth, high-packet-loss conditions. We propose a corruption-resilient latent space reconstruction framework that transforms corrupted latent variables into spatial priors, leveraging ego-vehicle context to complete missing bird's-eye-view (BEV) features. A key innovation is its zero-shot generalization capability: trained exclusively under intact communication conditions, the framework seamlessly adapts to severe packet loss scenarios without requiring multi-condition fine-tuning. Experimental results demonstrate that our approach achieves state-of-the-art robustness at a 90% packet loss rate while reducing communication overhead by 64× compared to dense fusion baselines, significantly outperforming existing methods.

Abstract

Given the inherent unpredictability of packet loss in vehicular wireless communications, V2X collaborative perception can yield practical benefits only if agents can achieve reliable collaboration under lossy and low-bandwidth communication conditions. Existing dense BEV feature fusion methods depend on redundant BEV feature exchange, which is infeasible in low-bandwidth scenarios, while compact-communication methods aggressively compress messages but can hardly recover the missing feature content after packet loss. In this paper, we present LR-V2X, a loss-resilient, latent-space reconstruction framework that converts corrupted received latents (even under severe 90% packet loss) into a spatial prior and then reconstructs the missing BEV information from this informative prior and using ego context as condition. Notably, the model can be trained under complete communication conditions and can be directly applied to lossy conditions at test time, eliminating the need for training under numerous lossy conditions. Experiments on DAIR-V2X and V2XREAL show that LR-V2X delivers the strongest robustness under severe packet loss and preserves reliable collaboration as communication quality degrades. And it reduces communication overhead by $64\times$ compared to dense BEV feature fusion baselines. Code will be released at https://github.com/sidiangongyuan/LR-V2X.

Problem

• V2X collaborative perception
• packet loss
• low-bandwidth communication
• BEV feature fusion
• loss resilience

Innovation

• Collaborative Perception
• Loss-Resilient
• Latent-Space Reconstruction
• V2X Communication
• BEV Feature Fusion