This letter investigates online high-definition (HD) map caching for autonomous driving in vehicular networks when vehicles’ requests and trajectories are unknown in advance. We first introduce a general HD map model that divides each HD map into different sub-maps to accommodate different driving functionalities. Then we introduce a service model for road side units (RSUs) by considering both the freshness of the dynamic sub-maps that are locally saved at each vehicle and the retrieving cost of sub-maps that are not cached at RSUs but must be needed for high-level driving control. After that, we propose a distributed multi-agent multi-armed bandit (MAMAB) algorithm for each RSU to learn its own cache strategy independently for maximizing the accumulated cache utility over a finite time horizon. Simulation results are provided to validate the effectiveness of our proposed algorithm.

中文翻译：

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自动驾驶系统中高清地图的分布式在线缓存

这封信调查了当车辆的请求和轨迹事先未知时，用于车载网络中自动驾驶的在线高清 (HD) 地图缓存。我们首先介绍一个通用的高清地图模型，该模型将每个高清地图划分为不同的子地图，以适应不同的驾驶功能。然后，我们通过考虑每辆车本地保存的动态子地图的新鲜度和未缓存在 RSU 处但必须需要的子地图的检索成本，为路侧单元 (RSU) 引入服务模型。高级驾驶控制。之后，我们为每个 RSU 提出了一种分布式多代理多臂强盗 (MAMAB) 算法，以独立学习自己的缓存策略，以在有限的时间范围内最大化累积的缓存效用。