In autonomous vehicle (AV) technology, the ability to accurately predict the
movements of surrounding vehicles is paramount for ensuring safety and
operational efficiency. Incorporating human decision-making insights enables
AVs to more effectively anticipate the potential actions of other vehicles,
significantly improving prediction accuracy and responsiveness in dynamic
environments. This paper introduces the Human-Like Trajectory Prediction (HLTP)
model, which adopts a teacher-student knowledge distillation framework inspired
by human cognitive processes. The HLTP model incorporates a sophisticated
teacher-student knowledge distillation framework. The "teacher" model, equipped
with an adaptive visual sector, mimics the visual processing of the human
brain, particularly the functions of the occipital and temporal lobes. The
"student" model focuses on real-time interaction and decision-making, drawing
parallels to prefrontal and parietal cortex functions. This approach allows for
dynamic adaptation to changing driving scenarios, capturing essential
perceptual cues for accurate prediction. Evaluated using the Macao Connected
and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighD
benchmarks, HLTP demonstrates superior performance compared to existing models,
particularly in challenging environments with incomplete data. The project page
is available at Github.

这篇论文介绍了一种人类似的轨迹预测模型（HLTP 模型），它采用了灵感自人类认知过程的教师 - 学生知识蒸馏框架，这种方法可以在动态环境中动态适应变化的驾驶场景，提高准确预测的关键知觉线索的获取能力。该模型在 Macao Connected and Autonomous Driving (MoCAD) 数据集以及 NGSIM 和 HighD 基准测试中展示出优于现有模型的性能，特别是在数据不完整的具有挑战性的环境中。