A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information

Trajectory prediction is an essential component in autonomous driving systems, as it can forecast the future movements of surrounding vehicles, thereby enhancing the decision-making and planning capabilities of autonomous driving systems. Traditional models relying on constant acceleration and constant velocity often experience a reduction in prediction accuracy as the forecasted timeframe extends. This limitation makes it challenging to meet the demands for medium to long-term trajectory prediction. Conversely, data-driven models, particularly those based on Long Short-Term Memory (LSTM) neural networks, have demonstrated superior performance in medium to long-term trajectory prediction. Therefore, this study introduces a hierarchical LSTM-based method for vehicle trajectory prediction. Considering the difficulty of using a single LSTM model to predict trajectories for all driving intentions, the trajectory prediction task is decomposed into three sequential steps: driving intention prediction, lane change time prediction, and trajectory prediction. Furthermore, given that the driving intent and trajectory of a vehicle are always subject to the influence of the surrounding traffic flow, the predictive model proposed in this paper incorporates the interactional information of neighboring vehicle movements into the model input. The proposed method is trained and validated on the real vehicle trajectory dataset Next Generation Simulation. The results show that the proposed hierarchical LSTM method has a lower prediction error compared to the integral LSTM model.