用多个路侧激光雷达+边缘计算，提升自动驾驶3D检测效率与隐私。
SC-MII: Infrastructure LiDAR-based 3D Object Detection on Edge Devices for Split Computing with Multiple Intermediate Outputs Integration
- 边缘设备处理部分网络层，将中间特征传给服务器
- 实测速度提升2.19倍，设备耗时减少71.6%，精度损失<1.09%
- 适合资源受限的自动驾驶系统部署
基于激光雷达点云数据与深度神经网络的3D目标检测在自动驾驶中至关重要。然而，将先进模型部署在边缘设备上面临高计算需求和能耗挑战，且单个激光雷达存在盲区问题。本文提出SC-MII：基于多基础设施激光雷达的边缘设备3D目标检测框架，支持分治计算与多中间输出融合。在该方案中，边缘设备通过前几层DNN处理本地点云，并将中间特征发送至边缘服务器；服务器整合特征并完成推理，有效降低延迟与设备负载，同时增强隐私保护。在真实世界数据集上的实验表明，该方法实现2.19倍的速度提升，边缘设备处理时间减少71.6%，精度最多下降1.09%。
原文摘要 · Abstract (English)
3D object detection using LiDAR-based point cloud data and deep neural networks is essential in autonomous driving technology. However, deploying state-of-the-art models on edge devices present challenges due to high computational demands and energy consumption. Additionally, single LiDAR setups suffer from blind spots. This paper proposes SC-MII, multiple infrastructure LiDAR-based 3D object detection on edge devices for Split Computing with Multiple Intermediate outputs Integration. In SC-MII, edge devices process local point clouds through the initial DNN layers and send intermediate outputs to an edge server. The server integrates these features and completes inference, reducing both latency and device load while improving privacy. Experimental results on a real-world dataset show a 2.19x speed-up and a 71.6% reduction in edge device processing time, with at most a 1.09% drop in accuracy.
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