  Accurately and safely predicting the trajectories of surrounding vehicles is
essential for fully realizing autonomous driving (AD). This paper presents the
Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive
processes to improve trajectory prediction in AD. HLTP++ incorporates a novel
teacher-student knowledge distillation framework. The "teacher" model equipped
with an adaptive visual sector, mimics the dynamic allocation of attention
human drivers exhibit based on factors like spatial orientation, proximity, and
driving speed. On the other hand, the "student" model focuses on real-time
interaction and human decision-making, drawing parallels to the human memory
storage mechanism. Furthermore, we improve the model's efficiency by
introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for
faster and more precise predictions with fewer parameters. Evaluated using the
NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance
compared to existing models, which reduces the predicted trajectory error with
over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++
demonstrates strong adaptability in challenging environments with incomplete
input data. This marks a significant stride in the journey towards fully AD
systems.
