发表机构
Renmin University of China; The Hong Kong University of Science and Technology (HKUST)（中国人民大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LR-V2X抗损隐空间重建框架，可在低带宽通信及90%严重丢包下实现可靠V2X协同感知，通信开销较密集BEV特征融合降低64倍，无需在有损条件下训练。
AI 中文摘要
鉴于车载无线通信中数据包丢失的固有不可预测性，V2X协同感知只有在智能体能在有损且低带宽通信条件下实现可靠协作时才能产生实际效益。现有的密集BEV特征融合方法依赖冗余的BEV特征交换，这在低带宽场景下不可行；而紧凑通信方法虽对消息进行了激进压缩，但在数据包丢失后几乎无法恢复缺失的特征内容。本文提出LR-V2X，一种抗损的隐空间重建框架，该框架将受损的接收隐变量（即使在90%的严重数据包丢失情况下）转换为空间先验，再利用该信息先验并以自身上下文为条件重建缺失的BEV信息。值得注意的是，该模型可在完整通信条件下训练，测试时可直接应用于有损条件，无需在大量有损条件下训练。在DAIR-V2X和V2XREAL数据集上的实验表明，LR-V2X在严重数据包丢失下具备最强的鲁棒性，且随着通信质量下降仍能保持可靠协作，与密集BEV特征融合基线相比，其通信开销降低了64倍。代码将发布在该https URL。
英文摘要
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.
