发表机构
Chalmers University of Technology; Politecnico di Torino（查尔姆斯理工大学; 都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ITS Fairy通过读取接收方已报告内容而非预测感知，仅发送冲突相关缺失对象，在仿真中显著提升最小TCA，验证了应用层遮挡辅助的有效性。
AI 中文摘要
协同感知可以暴露车辆车载传感器范围之外的对象状态，但传感遮挡仍可能使本地安全应用缺少其碰撞计算所需的对象。为应对这一挑战，我们提出了ITS Fairy，一种基础设施侧服务器本地动态地图（S-LDM）服务，其决策单元是（接收方，缺失的冲突相关对象）对：在接收方基于CPM（协作感知消息）报告的意识中缺失的对象里，它仅发送那些与最接近时间（TCA）冲突测试相关的对象。类似的服务预测车辆能感知到什么；而ITS Fairy则读取它已经报告的内容。接收方将选定的状态插入其本地LDM，并使用其未改变的防碰撞控制器。我们在SUMO--ms-van3t--S-LDM仿真中评估了这种应用层机制，该仿真已扩展为VaN3Twin，使用了传感遮挡的车道合并和四向交叉口场景。在每个主扫描速度下，每次遭遇的最小辅助最小TCA都超过存档数据中最大的仅本地值。此外，辅助中位数保持在多秒范围内，而仅本地操作则反复接近零。在车道合并鲁棒性数据中，中位数收益在配置的80%辅助省略且分析频率为10和5赫兹时持续存在，但在100-120公里/小时且80%省略与1赫兹分析结合时基本消失。这些结果证明了针对接收方自身报告内容选择对象状态的应用层价值。它们不是车载无线信道评估，也不量化相对于转发每个附近对象的选择性节省了多少。
英文摘要
Cooperative perception can expose object state beyond a vehicle's onboard sensors, but sensing occlusion can still leave a local safety application without the objects its collision computation needs. To tackle this challenge, we present the ITS Fairy, an infrastructure-side Server Local Dynamic Map (S-LDM) service whose decision unit is the pair (recipient, missing conflict-relevant object): among objects absent from a recipient's CPM-derived reported awareness, it sends only those relevant to a Time of Closest Approach (TCA) conflict test. Comparable services predict what a vehicle can perceive; the ITS Fairy instead reads what it has already reported. The recipient inserts the selected state into its local LDM and uses its unchanged collision-avoidance controller. We evaluate this application-level mechanism in SUMO--ms-van3t--S-LDM emulation, since extended as VaN3Twin, using a sensing-occluded lane merge and four-way intersection scenario. At every main-sweep speed, the smallest assisted per-encounter minimum TCA exceeds the largest local-only value in the archived data. Additionally, assisted medians remain in the multi-second range where local-only operation repeatedly approaches zero. In the lane-merge robustness data, the median benefit persists at 80% configured assistance omission with 10 and 5 Hz analysis, but largely disappears at 100-120 km/h when 80% omission is combined with 1 Hz analysis. These results demonstrate the application-level value of supplying object state selected against what a recipient has itself reported. They are not a vehicular wireless-channel evaluation, and they do not quantify what selectivity saves relative to forwarding every nearby object.
Commentssubmitted to VTC2027-Spring
