Group Vehicle Trajectory Prediction With Global Spatio-Temporal Graph

Vehicle trajectory prediction is a challenging problem in the field of autonomous driving, which is of great significance to the safety of autonomous driving and traffic roads. In view of the interaction between surrounding vehicles and target vehicle and its own trajectory, we propose a new graph network model to predict future vehicle trajectory. First, the correlation network of vehicles at each time is constructed based on the complex network method. In order to make up for the lack of real spatial relevance caused by the fixed graph, we propose an adaptive parameter matrix to coordinate and optimize the global spatio-temporal graph. Second, the global spatio-temporal features of vehicle historical trajectory data are extracted by stacked graph convolution module. Finally, the obtained graph features are coded based on seq2seq network, and the trajectory prediction of road vehicles at different times in the future is realized. Our model has been trained and verified on the published NGSIM US-101 and I-80 data sets. Compared with other advanced schemes, our model has more accurate results in the future time of 5 seconds. In predicting the future group trajectory of vehicles on the road, the accuracy of long-term prediction is 16.6% higher than that of the most advanced scheme.