Methods for predicting vehicle trajectories in motorway weaving zones based on driving risk fields
A vehicle trajectory prediction method fusing the traveling risk field and vehicle lane-changing intention was proposed to improve the accuracy of vehicle trajectory prediction in the interweaving area.Firstly,the driving demand changes of drivers in the interweaving zone were analyzed,and the driving risk field model was used to uniformly represent the interaction risk when vehicles were driving;secondly,the Hidden Markov Model was used to identify the vehicle lane-changing intention;in addition,the input features were extended and fused in multiple dimensions by the Deep Belief Networks Online Learning Machine(DBN_OSELM)model,to improve the accuracy of the trajectory prediction in the interweaving zone.Finally,the proposed method was evaluated based on the CitySim dataset.The results show that the model can predict vehicle trajectories in the interweaving zone of highways with high accuracy,and the root mean square error(RMSE)of vehicle trajectory prediction for the three types(merging in the confluence area,maintaining the interweaving area,and driving out the diverging area)of driving needs of drivers in the interweaving zone are 0.683 5,0.257 4,and 0.631 5,respectively,and the average displacement error(ADE)is 0.46,0.21,and 0.48 m,respectively.The research results are helpful to improve the accuracy of vehicle trajectory prediction in complex scenarios and improve traffic safety in the intertwined area.
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