Joint Spatiotemporal-Frequency-Aware Feature Fusion for Vehicle Trajectory Prediction

To guarantee rational decision-making and safety of intelligent transportation systems and autonomous driving, existing vehicle trajectory prediction (VTP) methods extract spatial and temporal features from complex traffic environments to achieve accurate forecast results. However, they generally do not use the frequency-domain information inherently embedded in vehicle trajectory data, resulting in lower prediction accuracy. To solve this problem, we propose a joint spatiotemporal-frequency-aware feature fusion (STFA-FF) method for VTP. First, an intention recognition network is proposed to integrate spatial features and temporal features to infer driving intentions with high precision. Second, to fully utilize frequency-domain features, we present a multiscale frequency-domain feature extraction (MSFDFE) module to map vehicle trajectory data into the frequency domain, incorporate the high-frequency attenuation mask to suppress high-frequency noise, and deeply integrate short-term variations with long-term trends. In addition, a frequency-domain channel selection (FDCS) module is proposed to dynamically select key frequency channels related to driving modes. Furthermore, a multidomain feature fusion prediction network is proposed to process the spatial, temporal, and frequency-domain features to generate the final trajectory prediction results. Finally, experimental results demonstrate that the proposed method significantly outperforms mainstream approaches in prediction accuracy and robustness.