Vehicle multi-intention motion trajectory prediction based on Informer
Autonomous vehicles must accurately predict the trajectories of nearby vehicles to en-hance safety and comfort and facilitate rational decision-making.This study utilized a neural net-work approach to develop a multi-intention trajectory prediction model based on the Informer model and achieve accurate vehicle trajectory prediction.The model employs an encoder-decoder structure,using historical information from the traffic scene as input data and generating the vehicle's multi-in-tention-predicted trajectory as output.The encoder of the model utilizes an interactive information extraction network to extract vehicle interaction information by considering the dependency relation-ships.Subsequently,the decoder predicts a multi-intention trajectory that reflects various driving in-tentions based on the output of the encoder.Through training,validation,and testing on the HighD dataset of highway trajectories,the experimental results demonstrated the accurate prediction capabil-ity of the proposed multi-intention trajectory prediction model for target vehicles.Moreover,it out-performed the long short-term memory network-based trajectory prediction model in terms of accura-cy.The inclusion of an interactive information extraction network enhanced the accuracy of the mod-el predictions.Furthermore,generating multiple trajectories embodying distinct driving intentions contributes to objectively capturing the actual trajectory distribution,thereby enhancing vehicle safe-ty.In addition,a supplementary experiment was conducted to assess the lane change intention using predicted trajectories.The proposed model achieved 97%accuracy in predicting lane changes 3 s pri-or,indirectly indicating its outstanding trajectory prediction performance.
万方数据
维普
