车辆-边缘协同多传感器数据融合用于自动驾驶车辆远程操作的情形
Case for Vehicle-Edge Collaborative Multi-Sensor Data Fusion for Autonomous Vehicle Teleoperation
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中文总结 AI 辅助
本文提出SHARDED框架，通过车辆-边缘协同的特征级融合及两种机制，在减少上行链路流量和延迟的同时保持感知质量，适用于自动驾驶远程操作。
中文摘要 AI 辅助
当自动驾驶车辆（AV）遇到超出其运行设计域的场景时，远程操作提供了关键的安全后备方案。然而，在实践中，远程操作员主要依赖通过5G传输的压缩摄像头流，这些流在复杂动态环境中往往缺乏深度和空间几何线索，难以安全操作。虽然多传感器融合可以增强态势感知，但由于5G上行链路带宽和延迟限制，直接传输原始摄像头和激光雷达数据是不切实际的。在本文中，我们提出SHARDED，一种协作式摄像头-激光雷达感知框架，该框架在车辆和边缘之间部署特征级融合流水线，以减少上行链路流量，同时保持3D检测和深度估计精度。我们进一步为SHARDED设计了两种互补机制：（i）一种网络感知的自适应特征传输机制，与原始传感器数据相比，平均减少50%的数据流量（峰值超过95%），以及（ii）一种延迟感知的位置漂移补偿机制，以减轻由不稳定网络条件引起的跨模态错位。在nuScenes数据集和真实5G测量轨迹上的评估表明，SHARDED在降低上行链路带宽消耗和端到端延迟的同时，实现了有竞争力的感知质量。
英文摘要
Teleoperation provides a critical safety fallback when autonomous vehicles (AVs) encounter scenarios that are outside their operational design domain. In practice, however, remote operators rely primarily on compressed camera streams over 5G, which often lack depth and spatial geometric cues for safe operation in complex dynamic environments. While multi-sensor fusion can enhance situational awareness, directly transmitting raw camera and LiDAR data is impractical due to 5G uplink bandwidth and latency constraints. In this paper, we propose SHARDED, a collaborative camera-LiDAR perception framework that deploys a feature-level fusion pipeline across the vehicle and edge to reduce uplink traffic while preserving 3D detection and depth estimation accuracy. We further design two complementary mechanisms for SHARDED: (i) A network-aware adaptive feature transmission mechanism that reduces data traffic by 50% on average (peaking at over 95%) compared to raw sensor data, and (ii) a latency-aware positional drift compensation mechanism to mitigate cross-modal misalignment induced by unstable network conditions. Evaluations on the nuScenes dataset and real-world 5G measurement traces show that SHARDED achieves competitive perception quality while reducing uplink bandwidth consumption and end-to-end latency.
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。
