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Efficient 3D Road Map Data Exchange for Intelligent Vehicles in Vehicular Fog Networks
arXiv - CS - Networking and Internet Architecture Pub Date : 2020-01-13 , DOI: arxiv-2001.04057
Ivan Wang-Hei Ho, Sid Chi-Kin Chau, Elmer R. Magsino, Kanghao Jia

Through connecting intelligent vehicles as well as the roadside infrastructure, the perception range of vehicles can be significantly extended, and hidden objects at blind spots can be efficiently detected and avoided. To realize this, accurate road map data must be downloaded in real time to these intelligent vehicles for navigation and localization purposes. Besides, the cloud must be updated with dynamic changes that happened in the road network. These involve the transmissions of high-definition 3D road map data for accurately representing the physical environments. In this work, we propose solutions under the fog computing architecture in a heterogeneous vehicular network to optimize data exchange among intelligent vehicles, the roadside infrastructure, as well as regional databases. Specifically, the efficiency of 3D road map data dissemination at roadside fog nodes is achieved by exploiting index coding techniques to reduce the overall data load, while opportunistic scheduling of heterogeneous transmissions can be done to judiciously manage network resources and minimize operating cost. In addition, 3D point cloud coding and hashing techniques are applied to expedite the updates of various dynamic changes in the network. We empirically evaluate the proposed solutions based on real-world mobility traces of vehicles and 3D LIght Detection And Ranging (LIDAR) data of city streets. The proposed system is also implemented in a multi-robotic testbed for practical evaluation.

中文翻译:

车载雾网中智能车辆的高效 3D 路线图数据交换

通过连接智能车辆和路边基础设施,可以显着扩大车辆的感知范围,有效检测和避开盲点处的隐藏物体。为了实现这一点,必须将准确的道路地图数据实时下载到这些智能车辆中,用于导航和定位。此外,云必须根据道路网络中发生的动态变化进行更新。这些涉及传输高清 3D 路线图数据,以准确表示物理环境。在这项工作中,我们提出了异构车载网络雾计算架构下的解决方案,以优化智能车辆、路边基础设施和区域数据库之间的数据交换。具体来说,通过利用索引编码技术来减少整体数据负载,从而实现路侧雾节点 3D 道路地图数据传播的效率,同时可以进行异构传输的机会调度,以明智地管理网络资源并最小化运营成本。此外,还应用了 3D 点云编码和哈希技术来加快网络中各种动态变化的更新。我们根据车辆的真实移动轨迹和城市街道的 3D 光检测和测距 (LIDAR) 数据,凭经验评估所提出的解决方案。所提出的系统还在多机器人测试台中实施以进行实际评估。同时可以对异构传输进行机会调度,以明智地管理网络资源并最大限度地降低运营成本。此外,还应用了 3D 点云编码和哈希技术来加快网络中各种动态变化的更新。我们根据车辆的真实移动轨迹和城市街道的 3D 光检测和测距 (LIDAR) 数据,凭经验评估所提出的解决方案。所提出的系统还在多机器人测试台中实施以进行实际评估。同时可以对异构传输进行机会调度,以明智地管理网络资源并最大限度地降低运营成本。此外,还应用了 3D 点云编码和哈希技术来加快网络中各种动态变化的更新。我们根据车辆的真实移动轨迹和城市街道的 3D 光检测和测距 (LIDAR) 数据,凭经验评估所提出的解决方案。所提出的系统还在多机器人测试台中实施以进行实际评估。我们根据车辆的真实移动轨迹和城市街道的 3D 光检测和测距 (LIDAR) 数据,凭经验评估所提出的解决方案。所提出的系统还在多机器人测试台中实施以进行实际评估。我们根据车辆的真实移动轨迹和城市街道的 3D 光检测和测距 (LIDAR) 数据,凭经验评估所提出的解决方案。所提出的系统还在多机器人测试台中实施以进行实际评估。
更新日期:2020-04-03
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