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- W4328005950 abstract "Route planning have a huge impact on the safe driving and energy efficiency of autonomous vehicle, which is an inevitable part of Internet of Vehicle. In the 5G edge environment, autonomous vehicle needs to communicate with other vehicles and base stations, route planning needs to consider the communication overhead of autonomous vehicle in addition to the driving distance. However, the current route planning methods only consider the shortest path, which cannot comprehensively consider the distance overhead and time cost. To cope with these challenges, this paper proposes an algorithm based on reinforcement learning to solve autonomous vehicle route planning through 5G networks and edge computing(RLVRP), which can obtain the goal of minimizing the driving distance based on minimizing the task processing time and server response time. In addition, we update our route planning strategy according to the dynamic change of network resources. Extensive experimental results show that the proposed algorithm greatly reduces service delay compared with state-of-the-art baselines." @default.
- W4328005950 created "2023-03-22" @default.
- W4328005950 creator A5020305189 @default.
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- W4328005950 date "2022-08-01" @default.
- W4328005950 modified "2023-09-23" @default.
- W4328005950 title "A Route Planning for Autonomous Vehicle in 5G and Edge Computing Environment" @default.
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- W4328005950 doi "https://doi.org/10.1109/bigcom57025.2022.00044" @default.
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