Abstract: The prediction of vehicle trajectory has great significance in the autonomous vehicles and internet of ve-hicles systems． Vehicle trajectory prediction can help to judge the future motion state of vehicles and to avoid colli-sion． Therefore，a vehicle lane-change trajectory prediction model based on generative adversarial networks was suggested． Vehicle lane-changing data was collected with High-precision GPS instruments through complete vehicle test in urban highways． On this basis，a trajectory prediction model based on the generative adversarial networks was established． The generator of GAN adopts the LSTM encoder-decoder structure，and the future lane-changing trajectory is generated through the decoder by inputting the given observed lane-changing trajectories． By construc-ting neural network based on the MLP，the discriminative model can distinguish the generated trajectory and the target trajectory through multiple discriminating methods． By jointly training generative model and discriminative model，the future trajectory of single vehicle in real time can be predicted． Through cross-validation and model
comparison，the effects of historical trajectories and prediction trajectories of different lengths on prediction accura-cy were analyzed，and the validity and accuracy of the model was verified． The results show that，compared with the traditional model，our model can predict the lane-change trajectory over a long period of time with an obviously improved accuracy．

Key words: vehicle lane-changing, trajectory prediction, generative adversarial networks, LSTM encoder-decoder