  As autonomous driving technology progresses, the need for precise trajectory
prediction models becomes paramount. This paper introduces an innovative model
that infuses cognitive insights into trajectory prediction, focusing on
perceived safety and dynamic decision-making. Distinct from traditional
approaches, our model excels in analyzing interactions and behavior patterns in
mixed autonomy traffic scenarios. It represents a significant leap forward,
achieving marked performance improvements on several key datasets.
Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next
Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on
the Macao Connected Autonomous Driving (MoCAD) dataset. Our proposed model
shows exceptional proficiency in handling corner cases, essential for
real-world applications. Moreover, its robustness is evident in scenarios with
missing or limited data, outperforming most of the state-of-the-art baselines.
This adaptability and resilience position our model as a viable tool for
real-world autonomous driving systems, heralding a new standard in vehicle
trajectory prediction for enhanced safety and efficiency.
