论文标题
EDGEMAP：汽车边缘计算中的众包高清图
EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing
论文作者
论文摘要
高清晰度（HD）地图需要经常更新以捕获道路变化，这受到有限的专业收集工具的约束。为了维护最新地图，我们探索来自连接车辆的众包数据。但是，在动态网络中的传输和计算资源下，协作更新地图是有挑战性的。在本文中，我们提出了EDGEMAP，这是一个众包高清图，以最大程度地减少网络资源的使用，同时保持延迟要求。我们设计了一种日期算法，以在较小的时间尺度上适应车辆数据，并通过利用多代理的深入强化学习和高斯流程回归来在较大的时间范围内保留网络资源。我们在时间驱动的端到端模拟器中通过广泛的网络模拟评估EDGEMAP的性能。结果表明，与最先进的解决方案相比，EDGEMAP减少了30％以上的资源使用情况。
High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles. To maintain an up-to-date map, we explore crowdsourcing data from connected vehicles. Updating the map collaboratively is, however, challenging under constrained transmission and computation resources in dynamic networks. In this paper, we propose EdgeMap, a crowdsourcing HD map to minimize the usage of network resources while maintaining the latency requirements. We design a DATE algorithm to adaptively offload vehicular data on a small time scale and reserve network resources on a large time scale, by leveraging the multi-agent deep reinforcement learning and Gaussian process regression. We evaluate the performance of EdgeMap with extensive network simulations in a time-driven end-to-end simulator. The results show that EdgeMap reduces more than 30% resource usage as compared to state-of-the-art solutions.
