Vehicle trajectory prediction based on spatio-temporal information fusion in crowded driving scenario

The spatio-temporal interaction information among vehicles was integrated into the convolutional social pooling network to formulate a human-driving vehicle trajectory prediction model in the crowded driving scenario. The long short term memory (LSTM) network was used to predict the speeds of the crowded vehicles. The prediction result was used to calculate the speed differences among the vehicles. The LSTM encoder was built to capture the time-series features of the crowded vehicle trajectories. The convolutional social pooling network was designed to captured the spatial dependence of the crowded vehicles. The emerging probabilities of all possible movements of the vehicles and corresponding trajectories were predicted by the LSTM decoder. The movement with the highest emerging probability and its trajectory were taken as final prediction result of trajectory. The real vehicle trajectory dataset was used in the parameter calibration and performance verification of the proposed model. Different methods of trajectory encoding/decoding and speed predicting were tested to figure out their influences on the model performance. The test results were used to identify the optimal model structure. Calculation results show that compared with historical speed, predicted speed used to calculate speed difference as model input can decrease by 19.45% in terms of root mean square error (RMSE). Compared with the gate recurrent unit, the LSTM network as speed predictor can decrease by 4.91% in terms of RMSE. Compared with the original convolutional social pooling network, the trajectory prediction errors of the proposed model respectively decrease by 20.32% and 21.04% in terms of RMSE and negative log-likelihood. The model performance is also significantly better than other variants of the original convolutional social pooling network. The computation time difference of the proposed model and original convolutional social pooling network is about 3 ms, which meets the request of real-time application. 8 tabs, 9 figs, 23 refs.