Enhanced Multi-Vehicle Trajectory Prediction via an Extended Temporal Sequence Fusion Attention Network

Vehicle trajectory prediction is gaining significant attention from academia and industry because of its vital role in autonomous driving. However, current theories face two challenges. First, they generally underperform when faced with longer historical trajectory inputs, especially in large-scale scenarios. Second, they usually ignore the temporal continuity of the target vehicle itself. To address these issues, our study proposed a novel extended temporal sequence fusion attention (ETSFA) network. This network can fully capture the information from the historical trajectory and the dynamic influences of adjacent agents. In addition, the novel dual-channel decoupled model can precisely characterize the intricate spatiotemporal interplay of on-road vehicles. Specifically, the proposed network consists of two main parts. For temporal analysis, the linear inference network (LIN) is reparameterized into complex diagonal forms at the state–space model (SSM) layer to express the linear recurrence capability, thus effectively mining the long-term historical trajectory temporal features of the target vehicle. For spatial analysis, an advanced spatial perception module (SPM) based on graph attention networks (GATs) is proposed to aggregate vehicle and intervehicle interaction features. In addition, a spatial inference module (SIM) based on a convolutional linear inference unit (CONVLIN) is customized for spatiotemporal graph features. Finally, the proposed ETSFA is trained and validated across diverse public datasets, including HighD and NGSIM, demonstrating a marked improvement in the prediction accuracy of the proposed ETSFA over existing methods.