Prediction model of intelligent connected vehicles driving behavior based on deep learning multi network fusion

Abstract

The accurate prediction of driving intention and driving tracks of surrounding vehicles is the basis to ensure the safe driving of Intelligent Connected Vehicles in complex road scenes. This paper proposes a driving intention and behavior prediction model of intelligent connected vehicles based on BLSTM-DCG-ATT by combining Bi-directional Long Short-Term Memory, Deep Convolutional Generic Adversary and Attention Mechanism. The data with dual characteristics are obtained through the forward and reverse Bi-directional LSTM link and attention mechanism, and then the characteristic data are convolved through the Deep Convolutional Generic Adversary to iteratively generate the lane change intention and driving data of the intelligent connected vehicle and surrounding vehicles in the future. The simulation results show that the model can accurately predict the lane change intention and driving track of the intelligent connected vehicle and its surrounding vehicles under the condition of complex road network and dense traffic flow, and the prediction accuracy reaches 94%.

Keywords

• intelligent transportation|vehicle behavior prediction|dcgan|intelligent connected vehicle|bi-directional long short-term memory