LiveMap：汽车边缘计算中的实时的动态地图
LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
摘要：
自动驾驶需要各种视线传感器来感知在各种环境不确定性（如视觉遮挡和极端天气）下可能受损的周围环境。为了提高驾驶安全性，我们探索在汽车边缘计算网络内的联网车辆之间无线共享感知信息。然而，在动态网络条件和变化的计算工作负载下，实时的共享大量感知数据具有挑战性。在本文中，我们提出了LiveMap，这是一个实时的动态地图，它可以在亚秒级的时间内利用来自联网车辆的众包数据检测、匹配和跟踪道路上的物体。我们开发了LiveMap的数据平面，通过目标检测、投影、特征提取、对象匹配有效地处理单个车辆数据，并通过对象组合有效地集成来自多个车辆的对象。我们设计了LiveMap的控制平面，允许车辆计算的自适应卸载，并开发了一种智能车辆调度和卸载算法，以减少基于深度强化学习（DRL）技术的车辆卸载延迟。我们在一个小规模的测试平台上实现了LiveMap，并开发了一个大规模的网络模拟器。通过实验和仿真对LiveMap的性能进行了评估，结果表明LiveMap比基线方案平均延迟降低了34.1%.
英文摘要：
Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme weather. To improve driving safety, we explore to wirelessly share perception information among connected vehicles within automotive edge computing networks. Sharing massive perception data in real time, however, is challenging under dynamic networking conditions and varying computation workloads. In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the data plane of LiveMap that efficiently processes individual vehicle data with object detection, projection, feature extraction, object matching, and effectively integrates objects from multiple vehicles with object combination. We design the control plane of LiveMap that allows adaptive offloading of vehicle computations, and develop an intelligent vehicle scheduling and offloading algorithm to reduce the offloading latency of vehicles based on deep reinforcement learning (DRL) techniques. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator. We evaluate the performance of LiveMap with both experiments and simulations, and the results show LiveMap reduces 34.1% average latency than the baseline solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
