DRL-MURA: A Joint Optimization of High-Definition Map Updating and Wireless Resource Allocation in Vehicular Edge Computing Networks

Lili Nie, Huiqiang Wang, Guangsheng Feng, Hongwu Lv, Hang Cui

Abstract: High-definition (HD) map caching at roadside units (RSUs) is an important component of localization for self-driving vehicles, HD map content delivery services must be efficient for various self-driving vehicles. Nevertheless, HD maps are dynamic files that must be updated and replenished in real time. Developing an effective content delivery strategy for different types of self-driving vehicles to require HD maps, while ensuring safe driving and minimizing bandwidth consumption, is challenging. To maximize the monetary utility of the vehicle system, in this article, we jointly optimize the HD map update strategy and the wireless bandwidth resource allocation strategy, considering service delay constraints and overall system risk. However, the optimization problem described above is an NP-hard mixed-integer nonlinear programming (MINLP) problem. Additionally, in a self-driving vehicle scenario, the highly dynamic character of HD maps, the diversity of self-driving vehicle types, and the randomness of vehicle trajectories are unknown to the vehicle system in advance. The intractable optimization problem and the highly uncertain nature of the driving environment make it difficult to find an existing method that allows vehicles to obtain HD maps that meet their localization requirements in a timely manner. To address the above issues, we propose DRL-MURA, which can learn HD map updates and implement a wireless bandwidth resource allocation strategy by constantly interacting with environment based on a deep reinforcement learning (DRL) algorithm. Finally, we prove the accuracy and effectiveness of our method through simulation experiments.