Low Latency Scalable Point Cloud Communication in VANETs using V2I Communication
Low Latency Scalable Point Cloud Communication in VANETs using V2I Communication
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DOI：
10.1109/icc.2019.8761285
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Anique Akhtar;Junchao Ma;R. Shafin;Jianan Bai;Lianjun Li;Zhu Li;Lingjia Liu
中科院分区：
文献类型：
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作者：
Anique Akhtar;Junchao Ma;R. Shafin;Jianan Bai;Lianjun Li;Zhu Li;Lingjia Liu
移动的边缘和基于车辆的深度发送和实时点云通信是实现自动驾驶的重要子任务。在本文中，我们提出了一个点云多播在VANEQUALITY使用车辆到基础设施（V2 I）通信的框架。我们采用了一个可扩展的二叉树嵌入四叉树（BTQT）的点云源编码器与比特率弹性匹配的自适应随机网络编码（ARNC）组播不同的层的车辆。我们的BTQT编码的点云的可扩展性提供了一个折衷的接收体素的大小/质量与信道条件，而ARNC有助于最大限度地提高吞吐量下的硬延迟约束。该解决方案使用来自MERL的自动驾驶室外3D点云数据集进行测试。具有良好信道条件的用户接收接近无损的点云，而具有不良信道条件的用户仍然能够接收至少基本层点云。
Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.