IMA-LSTM: An Interaction-Based Model Combining Multihead Attention with LSTM for Trajectory Prediction in Multivehicle Interaction Scenario

The rapid development of vehicle-to-vehicle (V2V) communication technology provides more opportunities to improve traffic safety and efficiency, which facilitates the exchange of multivehicle information to mine potential patterns and hidden associations in vehicle trajectory prediction. To address the importance of fine-grained vehicle interaction modelling in vehicle trajectory prediction, the present work proposes an integrated vehicle trajectory prediction model that combines the multihead attention mechanism with long short-term memory (IMA-LSTM) in multivehicle interaction scenarios. Compared to existing studies, a dedicated feature extraction module including both individual features and interactive features is designed and sophisticated multihead attention mechanism is applied with LSTM framework to capture the variation of spatial-temporal interactions between vehicles. The performance of the proposed model in different scenarios is examined using both the high-D and the NGSIM dataset through comprehensive comparison experiments. The results indicate that the proposed IMA-LSTM model presents great improvement in vehicle trajectory prediction performance in different scenarios compared to the model that does not consider multivehicle interaction features. Moreover, it outperforms other existing models in 3–5s prediction horizons and such superiority is more evident in left lane-changing (LLC) scenarios than lane-keeping (LK) and right lane-changing (RLC) scenarios. The outcomes fully address the importance of fine-grained interactive feature modelling in improving vehicle trajectory performance in complex multivehicle interaction scenarios and could further contribute to more refined traffic safety and traffic efficiency management.