This paper introduces the Human-Like Trajectory Prediction (HLTP) model, which aims to enhance the trajectory prediction capabilities of autonomous vehicles (AVs) by incorporating human decision-making insights. HLTP employs a teacher-student knowledge distillation framework inspired by human cognitive processes, where the "teacher" model mimics the visual processing of the human brain, and the "student" model focuses on real-time interaction and decision-making. The model includes an adaptive visual sector and a surround-aware encoder to capture essential perceptual cues, and a lightweight framework to efficiently process spatio-temporal dynamics. Evaluations using the Macao Connected and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighB benchmarks, demonstrate that HLTP outperforms existing models, especially in challenging environments with incomplete data. The project page is available at [GitHub](https://github.com/haichengliao/HLTP).This paper introduces the Human-Like Trajectory Prediction (HLTP) model, which aims to enhance the trajectory prediction capabilities of autonomous vehicles (AVs) by incorporating human decision-making insights. HLTP employs a teacher-student knowledge distillation framework inspired by human cognitive processes, where the "teacher" model mimics the visual processing of the human brain, and the "student" model focuses on real-time interaction and decision-making. The model includes an adaptive visual sector and a surround-aware encoder to capture essential perceptual cues, and a lightweight framework to efficiently process spatio-temporal dynamics. Evaluations using the Macao Connected and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighB benchmarks, demonstrate that HLTP outperforms existing models, especially in challenging environments with incomplete data. The project page is available at [GitHub](https://github.com/haichengliao/HLTP).