AI 中文总结
针对集体感知服务中因消息传输导致的信道拥塞问题，提出基于价值的DCC设施层“质量”选择器，结合信息价值与对象级选择，经与现有方法对比测试，能在维持信道负载时保留更多高价值对象，改善关键感知信息传播。
AI 中文摘要
集体感知服务（CPS）能使智能交通系统站点（ITS-S）间交换传感器信息，但频繁传输集体感知消息（CPM）及其可变大小和其他车辆服务负载会导致严重信道拥塞。现有分布式拥塞控制（DCC）接入层机制未考虑CPM中对象的相对重要性，设施层DCC机制在异构环境中效果不佳。本文提出基于价值的DCC设施层“质量”选择器，将每位速率控制器的信息价值（VoI）与对象级选择相结合。与标准和文献中的现有方法进行基准测试，结果表明该方法在保持信道负载接近目标CBR的同时，比现有方法保留更多高VoI对象，从而改善关键感知信息的传播。
英文摘要
While the Collective Perception Service (CPS) enables the exchange of sensor information among Intelligent Transport System Stations (ITS-S'), frequent transmission of Collective Perception Messages (CPMs), their highly variable size, and load from other vehicular services can cause severe channel congestion. Existing Distributed Congestion Control (DCC) Access layer mechanisms typically regulate channel load without considering the relative importance of the objects carried in CPMs. This limits their ability to preserve high-value information under constrained radio resources. More recently, Facilities layer DCC mechanisms attempt to prioritise high value objects within the specified radio resource limits but may not operate well in heterogeneous environments where the number of sensed objects and their importance can vary significantly over time or between ITS-S'. This paper proposes a value-based DCC Facilities layer 'quality' selector that couples a Value of Information (VoI) per bit rate controller with object-level selection. It is benchmarked against state of the art approaches from standards and the literature, with results showing that the proposed method maintains channel load near the target CBR while retaining more high-VoI objects than state of the art approaches, thereby improving the dissemination of perception-critical information.
