  Trajectory prediction is a cornerstone in autonomous driving (AD), playing a
critical role in enabling vehicles to navigate safely and efficiently in
dynamic environments. To address this task, this paper presents a novel
trajectory prediction model tailored for accuracy in the face of heterogeneous
and uncertain traffic scenarios. At the heart of this model lies the
Characterized Diffusion Module, an innovative module designed to simulate
traffic scenarios with inherent uncertainty. This module enriches the
predictive process by infusing it with detailed semantic information, thereby
enhancing trajectory prediction accuracy. Complementing this, our
Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic
scenarios on vehicle dynamics across both spatial and temporal dimensions with
remarkable effectiveness. Demonstrated through exhaustive evaluations, our
model sets a new standard in trajectory prediction, achieving state-of-the-art
(SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone
(HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both
short and extended temporal spans. This performance underscores the model's
unparalleled adaptability and efficacy in navigating complex traffic scenarios,
including highways, urban streets, and intersections.
