网联车辆并线预测与巡航控制的研究

For detecting the driver's merging intention of the vehicle in adjacent lane and enhance the cruising active safety of connected vehicles, an iterative loop prediction algorithm based on nonlinear autoregressive(NAR)neural network learning is proposed. The training samples of NAR neural network are obtained from the merging data of vehicles in real traffic environment, the trained network is used to predict the lateral trajectory of the adjacent vehicle in a certain time-segment of future, and the cut-in probability of adjacent vehicle is calculated according to the designated monitoring area. Meanwhile, a follow-up distance strategy considering merging probability is also proposed and applied to the connected vehicle CACC system. The results show that the merging prediction algorithm proposed can accurately calculate the lateral lane change trajectory of adjacent vehicle, and the follow-up strategy proposed can enhance the follow-up safety of vehicle.