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.

翻译：在自动驾驶车辆（AV）技术中，准确预测周围车辆运动的能力对于确保安全性和运行效率至关重要。融入人类决策洞察有助于自动驾驶车辆更有效地预判其他车辆的潜在行为，显著提升动态环境中的预测精度和响应能力。本文提出类人轨迹预测（HLTP）模型，该模型采用受人类认知过程启发的师生知识蒸馏框架。HLTP模型集成了精密的师生知识蒸馏体系："教师"模型配备自适应视觉区域，模拟人脑视觉处理机制，尤其是枕叶和颞叶的功能；"学生"模型聚焦于实时交互与决策，类比前额叶和顶叶皮层的功能。该方法能够动态适应不断变化的驾驶场景，捕捉关键感知线索以实现精准预测。采用澳门互联自动驾驶（MoCAD）数据集，结合NGSIM和HighD基准测试进行评估，HLTP在现有模型对比中展现出优越性能，尤其在数据不完整的复杂环境中表现突出。项目页面已发布于Github。