论文标题
带有众包车辆的实时动态图
Real-Time Dynamic Map with Crowdsourcing Vehicles in Edge Computing
论文作者
论文摘要
自动驾驶的感知环境具有偏远的传感器，这些传感器在环境不确定性下受到损害。为了在高清图中获得实时全球信息，我们调查以共享连接和自动化车辆之间的感知信息。但是，在汽车边缘计算中不同的网络动态下实现实时感知共享是一项挑战。在本文中，我们提出了一张新颖的实时动态图，名为Livemap，以检测，匹配和跟踪道路上的对象。我们设计了LiveMAP的数据平面，以有效地处理单个车辆数据，其中包括多个顺序计算组件，包括检测，投影，提取，匹配和组合。我们设计了LiveMap的控制平面，以使用两种新算法（中央和分布式）实现自适应车辆卸载，以平衡基于深度强化学习技术的延迟和覆盖效果。我们通过在边缘网络模拟器上的小规模物理测试床和网络模拟上进行了两个现实实验进行广泛的评估。结果表明，在延迟，覆盖范围和准确性方面，LiveMAP明显优于现有的解决方案。
Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named LiveMap to detect, match, and track objects on the road. We design the data plane of LiveMap to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of LiveMap to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that LiveMap significantly outperforms existing solutions in terms of latency, coverage, and accuracy.
