Multimodal adversarial informer for highway vehicle lane-changing trajectory prediction

Abstract

To enhance the perception of vehicle trajectory information and lane-changing decision-making capabilities in intelligent connected vehicles during multivehicle interaction scenarios, we propose a novel method based on a Multimodal Adversarial Informer (MAI) for highway multivehicle lane-changingrajectory prediction. This method achieves spatiotemporal features of target and surrounding vehicles through graph learning of temporal features and spatial adjacency matrices. Considering the heading angle and vehicle local X-axis displacement, the vehicle trajectory samples are categorized for training and validation of the multimodal Informer. A multi-criterion discriminator is utilized to judge whether the generated trajectory fits the requirements of accuracy and rationality. After adversarial learning, the optimal vehicle lane-changing trajectory prediction is obtained using the proposed MAI. Experiments conducted with the NGSIM dataset demonstrate the comparative performance of baseline models on three different noise-added testing datasets using MAE, RMSE, and R² metrics. The MAI model consistently outperforms the others, achieving the lowest MAE and RMSE and the highest R² values across all datasets, indicating superior predictive accuracy and fit. Furthermore, the results show that the proposed MAI framework maintains a relatively low prediction error over both short-term and long-term horizons compared to baseline models.