通过语义感知协同感知扩展智能交通系统的安全 horizon
HMS-SCP: Task-Oriented Multi-Scale Semantic Communication for V2X Cooperative Perception
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中文总结 AI 辅助
本文提出 HMS-SCP 框架，通过多尺度语义冗余与 JSCC 编码实现带宽效率与鲁棒性的平衡，在 V2X 环境中保障安全关键型协同感知的性能。
中文摘要 AI 辅助
协同感知使车辆和基础设施能通过车万物联网（V2X）通信交换传感器数据，将感知覆盖范围扩展至遮挡区域之外，缓解盲区问题。尽管该技术对自动驾驶和安全至关重要，但实际部署常依赖带宽效率高的后期融合方案。近期，中间融合作为一种有前景的方法被提出，可实现带宽与精度的最优权衡。然而，在密集城市环境中，累积的带宽需求可能超出网络容量，进而危及安全关键型协同智能交通系统（C-ITS）的功能。为缓解这些问题，本文提出分层多尺度语义感知协同感知框架（HMS-SCP），这是一种鲁棒、抗噪且带宽高效的框架，适用于协同感知中面向任务的语义通信。HMS-SCP 采用空间重要性预测器识别各尺度下与任务相关的网格元素，随后将这些元素直接映射为复值符号以用于联合信源信道编码（JSCC）。与以往依赖高维符号投影实现鲁棒性的方法不同，HMS-SCP 利用多尺度间的结构语义冗余提升抗信道噪声的能力，同时保持极低的符号速率。该设计显著降低了带宽消耗，缓解了高密度车载环境中的网络拥塞。在模拟 OPV2V 数据集和真实 DAIR-V2X 数据集上开展的大量评估表明，HMS-SCP 可在严重瑞利衰落和极端压缩比下有效防止性能崩溃，维持高置信度的远场检测，实时延迟低于 16 毫秒，完全处于动态 V2X 环境的安全关键阈值范围内。
英文摘要
Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing coverage beyond occlusions and mitigating blind spots. While critical for autonomous driving and safety, practical deployments often rely on bandwidth-efficient late fusion. Recently, intermediate fusion has emerged as a promising approach for an optimal bandwidth-accuracy trade-off. However, in dense urban environments, cumulative bandwidth demands can overwhelm network capacity, potentially compromising safety-critical Cooperative Intelligent Transport Systems (C-ITS) functions. To alleviate these problems, this paper proposes Hierarchical Multi-Scale Semantic-Aware Cooperative Perception (HMS-SCP), a robust noise-resilient and bandwidth-efficient framework for task-oriented semantic communication in cooperative perception. HMS-SCP employs a spatial importance predictor to identify task-relevant grid elements at each scale, which are then directly mapped into complex-valued symbols for Joint Source-Channel Coding (JSCC). Unlike prior methods that rely on high-dimensional symbol projections for robustness, HMS-SCP exploits structural semantic redundancy across multiple scales to enhance resilience against channel noise, while maintaining an ultra-low symbol rate. This design significantly reduces bandwidth consumption and mitigates network congestion in high-density vehicular environments. Extensive evaluations on the simulated OPV2V and real-world DAIR-V2X datasets demonstrate that HMS-SCP effectively prevents performance collapse under severe Rayleigh fading and extreme compression ratio, maintaining high-confidence far-field detection with a real-time latency of below 16~ms, well within the safety-critical thresholds for dynamic V2X environments.
发表机构
- Monash University Malaysia（莫纳什大学马来西亚分校）
- Multimedia University（多媒体大学）
机构由 AI 辅助整理，请以论文原文为准。
