通过边缘协同提升多车感知精度，实时降低延迟。
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
- 在边缘服务器聚合多车检测数据，融合多视角信息。
- 相比单车方案，物体分类准确率最高提升75%。
- 适合对实时性要求高的自动驾驶系统使用。
准确可靠的物体检测对保障联网自动驾驶车辆（CAVs）的安全与效率至关重要。传统车载感知系统受限于遮挡和视觉盲区，而基于云的方案则引入显著延迟，难以满足动态环境中自动驾驶对实时处理的需求。为此，我们提出一种新型框架——边缘协同物体检测（ECOD），利用边缘计算与多车协作实现实时、多视角的物体检测。该框架集成两项核心算法：感知聚合与协作估计（PACE）和可变物体计数与评估（VOTE）。PACE 在边缘服务器上聚合多辆 CAV 的检测数据，增强视野受限场景下的感知能力；VOTE 采用基于共识的投票机制，融合多车数据以提升物体分类准确性。两项算法均在边缘端运行，确保低延迟与可靠决策。我们构建了基于硬件的测试平台，包含配备摄像头的机器人式 CAV 及边缘服务器，用于评估框架效能。实验结果表明，ECOD 在物体分类准确率上相较传统单视角车载方案最高提升75%，同时实现低延迟、边缘驱动的实时处理。本研究凸显了边缘计算在提升时延敏感型自主系统协同感知方面的潜力。
原文摘要 · Abstract (English)
Accurate and reliable object detection is critical for ensuring the safety and efficiency of Connected Autonomous Vehicles (CAVs). Traditional on-board perception systems have limited accuracy due to occlusions and blind spots, while cloud-based solutions introduce significant latency, making them unsuitable for real-time processing demands required for autonomous driving in dynamic environments. To address these challenges, we introduce an innovative framework, Edge-Enabled Collaborative Object Detection (ECOD) for CAVs, that leverages edge computing and multi-CAV collaboration for real-time, multi-perspective object detection. Our ECOD framework integrates two key algorithms: Perceptive Aggregation and Collaborative Estimation (PACE) and Variable Object Tally and Evaluation (VOTE). PACE aggregates detection data from multiple CAVs on an edge server to enhance perception in scenarios where individual CAVs have limited visibility. VOTE utilizes a consensus-based voting mechanism to improve the accuracy of object classification by integrating data from multiple CAVs. Both algorithms are designed at the edge to operate in real-time, ensuring low-latency and reliable decision-making for CAVs. We develop a hardware-based controlled testbed consisting of camera-equipped robotic CAVs and an edge server to evaluate the efficacy of our framework. Our experimental results demonstrate the significant benefits of ECOD in terms of improved object classification accuracy, outperforming traditional single-perspective onboard approaches by up to 75%, while ensuring low-latency, edge-driven real-time processing. This research highlights the potential of edge computing to enhance collaborative perception for latency-sensitive autonomous systems.
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