基于神经网络的车辆强制换道预测模型

Aiming at the problem of fast-speed and high risk of lane changing behavior on expressway, we focus on the ineviteable, freguent and serve mandatory lane-changing behaviors to improve the lane-changing model based on gated recurrent unit (GRU), and predict the decision-making behaviors of mandatony lane-changing. To verify the effectiveness of the model, adopt the next generation simulation (NGSIM) data as the training set and test set of the model. From this data, the lateral acceleration threshold is obtained to screen out the phenomenon of lateral swing of vehicles. The experimental results indicate that the optimized model could determine the location of mandatory lane change with an accuracy of 96.01%. The accuracy of the model is improved by 3.67% compared with the LSTM model, and is improved by 7.31% compared with the naive Bayes network.