发表机构
University of Minnesota Twin Cities（明尼苏达大学双城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于5G远程驾驶场景，探讨传感器数据压缩对边缘/云服务器中下游AI任务（目标识别与语义分割）性能的影响，发现压缩降低性能且敏感性因数据源和压缩级别而异，并确定了多模态任务的最优权衡点。
AI 中文摘要
远程操作，如远程驾驶，被认为是5G和下一代（NextG）网络的关键用例之一。在此背景下，机器人、自动驾驶车辆或其他自主智能体通过移动网络将传感器数据传输到边缘或云服务器，AI系统与人类操作员协作以提供态势感知并实现远程控制。在远程驾驶场景中，车辆配备有摄像头阵列和LiDAR设备，这些设备可产生数百Mbps（兆比特每秒）的数据。如现有测量研究所示，此类数据量远超当前部署的5G网络的\u201c上行链路\u201d容量，尤其是当多辆车竞争无线资源时。因此，数据压缩势在必行。在本文中，我们探讨了传感器数据压缩对运行在边缘/云服务器中的下游AI任务性能的影响，这些任务对于提醒人类操作员实现安全远程操作至关重要。我们以目标识别和语义分割作为两个示例AI任务，研究数据压缩如何影响使用单模态（视频或LiDAR）和多模态（视频+LiDAR）数据的这两个AI任务的性能。我们发现，有损数据压缩通常会降低AI任务的性能。这些AI任务的性能根据数据源类型和压缩级别表现出不同程度的敏感性。我们还通过实验确定了多模态视觉任务的最优权衡点。
英文摘要
Teleoperation, such as remote driving, is considered as a key use case of 5G and Next-Generation (NextG) networks. In this context, robots, autonomous vehicles, or other autonomous agents transmit sensor data over mobile networks to edge or cloud servers, where AI systems collaborate with human operators to provide situational awareness and enable remote control. In the case of teleoperated driving, vehicles are equipped with an array of cameras and LiDAR devices, which can generate 100s Mbps (megabits per second) of data. As shown in existing measurement studies, such data volumes far exceed the \emph{uplink} capacity of currently deployed 5G networks, especially when multiple vehicles compete for radio resources. Data compression is thus imperative. In this paper, we explore the impact of sensor data compression on the performance of downstream AI tasks running in edge/cloud servers, which are crucial to alert human operators for safe teleoperation. Using object recognition and semantic segmentation as two example AI tasks, we study how data compression affects the performance of these two AI tasks using unimodal (video or LiDAR) and multi-modal (video+LiDAR) data. We find that lossy data compression generally decreases the performance of AI tasks. The performances of these AI tasks exhibit differing degrees of sensitivity based on the types of data sources and levels of compression. We also empirically identify an optimal trade-off point for the multi-modal vision tasks.
CommentsPublished in IEEE CQR 2024
Journal ref2024 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pp. 25-30, 2024
DOI：10.1109/CQR62340.2024.10705883
