  Vehicle trajectory prediction is crucial for advancing autonomous driving and
advanced driver assistance systems (ADAS), enhancing road safety and traffic
efficiency. While traditional methods have laid foundational work, modern deep
learning techniques, particularly transformer-based models and generative
approaches, have significantly improved prediction accuracy by capturing
complex and non-linear patterns in vehicle motion and traffic interactions.
However, these models often overlook the detailed car-following behaviors and
inter-vehicle interactions essential for real-world driving scenarios. This
study introduces a Cross-Attention Transformer Enhanced Conditional Diffusion
Model (Crossfusor) specifically designed for car-following trajectory
prediction. Crossfusor integrates detailed inter-vehicular interactions and
car-following dynamics into a robust diffusion framework, improving both the
accuracy and realism of predicted trajectories. The model leverages a novel
temporal feature encoding framework combining GRU, location-based attention
mechanisms, and Fourier embedding to capture historical vehicle dynamics. It
employs noise scaled by these encoded historical features in the forward
diffusion process, and uses a cross-attention transformer to model intricate
inter-vehicle dependencies in the reverse denoising process. Experimental
results on the NGSIM dataset demonstrate that Crossfusor outperforms
state-of-the-art models, particularly in long-term predictions, showcasing its
potential for enhancing the predictive capabilities of autonomous driving
systems.
