Abstract
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.
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