Edge intelligence based digital twins for internet of autonomous unmanned vehicles

Abstract

It aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

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