An effective hybrid V2V/V2I transmission latency method based on LSTM neural network

Abstract

We propose an effective hybrid vehicle-to-vehicle/vehicle-to-infrastructure (V2V/V2I) transmission latency method based on a long short-term memory (LSTM) neural network to address transmission latency in the internet of vehicles. First, a traffic model is established, and the LSTM artificial neural network is used to predict the vehicle arrival rate in the road section. Second, the vehicle arrival rate function is used to construct an objective function, i.e., the problem of minimizing system transmission the overall latency. The hybrid V2V/V2I transmission method determines the communication transmission mode of the vehicles to minimize transmission latency. The simulation results show that the overall transmission latency is substantially lower for the hybrid V2V/V2I transmission method than the pure V2I transmission method with the transmission packet size and vehicle speed varying.