发表机构
North Carolina State University; New York University（北卡罗来纳州立大学; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对车载网络视觉数据传输难题，提出带残差量化的自适应语义通信架构，可在线调整传输策略，提升率失真性能、传输可靠性及下游感知性能。
AI 中文摘要
车载网络需要及时的视觉信息交换以支持协同感知、危险感知等安全关键应用，但在带宽受限且动态变化的无线链路上传输高维相机数据仍具挑战性。本文提出一种面向车载组播的自适应语义通信架构，该架构可根据当前信道状况、可用通信资源及延迟要求调整传输的视觉信息量。具体而言，视觉编码器采用残差量化将每张图像表示为多个码率与重建质量等级；运行时系统根据通信条件选择合适等级，在网络资源有限时以视觉保真度换取更低传输开销。上下文自适应熵编码利用离线学习的统计数据进一步压缩量化表示，无需针对不同信道条件重新训练。该设计使视觉表示可离线学习，通信策略则在线自适应调整。实验结果表明，该架构在超低码率下具备有竞争力的率失真性能，在各类车载场景中提升了传输可靠性与截止期限满足度，且具备优异的下游感知性能，在动态适配变化的通信条件时，mAP50最高可达0.88。
英文摘要
Vehicular networks require timely visual information exchange to support safety-critical applications such as cooperative perception and hazard awareness, yet transmitting high-dimensional camera data over bandwidth-limited and dynamic wireless links remains challenging. In this paper, we propose an adaptive semantic communication architecture for vehicular multicast that adjusts the amount of visual information transmitted according to current channel conditions, available communication resources, and latency requirements. Specifically, the visual encoder uses residual quantization to represent each image at multiple bitrate and reconstruction-quality levels. At runtime, the system selects an appropriate level based on the communication conditions, enabling it to trade visual fidelity for lower transmission overhead when network resources are limited. Context-adaptive entropy coding further compresses the quantized representations using offline-learned statistics, without requiring retraining for different channel conditions. This design allows the visual representation to be learned offline while the communication strategy is adapted online. Experimental results demonstrate competitive rate-distortion performance at ultra-low bitrates, improved delivery reliability and deadline satisfaction across diverse vehicular scenarios, and strong downstream perception performance, achieving up to 0.88 mAP50 while dynamically adapting to changing communication conditions.
