Research On Rating Decision Of 5G For Automatic Driving

Cloud computing has powerful computing power and can handle vehicle traffic that consumes a lot of computing resources,such as global path planning in autonomous driving.However,because the introductions of propagation delay time,cloud computing cannot make timely decisions on vehicle traffic that requires real-time decision-making such as model learning and partial path planning.Since the Mobile Edge Computing(MEC)is close to the vehicle terminal,it can provide low latency,location awareness services.Therefore,the MEC is introduced into the autopilot decision,and the vehicle traffic is graded and decided through the edge computing and cloud computing.In the automatic driving decision system,it is an urgent issue to be studied about how to allocate vehicle traffic on the edge of the network and the cloud on demand,and how to uniformly schedule traffic in the transmission network to ensure low delay and high efficiency of the hierarchical decision system.In order to solve the problem of traffic distribution between edge computing and cloud computing,this thesis constructs the problem of traffic distribution which based on the time delay and energy consumption in cloud network architecture.A low-complexity vehicle traffic distribution algorithm based on interior point method is proposed to solve the problem,which can make the method of distributing vehicle traffic at the edge of the network and the cloud is obtained and realize differentiated service for vehicle traffic and improves network resource utilization.In order to effectively dispatch vehicle traffic in the transmission network and improve network energy efficiency,this thesis models the traffic distribution problem of the transmission network,and proposes an efficient traffic scheduling algorithm based on dormancy about OF switch.Through the Software Defined Network(SDN)controller,and the OF switch with low utilization or idleness is put into a state of dormancy,the traffic transmission delay is reduced,and the energy efficiency of the transmission network is improved.