Predicting the movements of surrounding vehicles is crucial for autonomous vehicle (AV) technology’s safety and efficiency. The paper, A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving, details the Human-Like Trajectory Prediction (HLTP) model, utilizing a teacher-student knowledge distillation framework inspired by human cognition. This method captures essential perceptual cues from driving scenarios, enabling AVs to efficiently predict other vehicles’ actions. The HTLP’s performance, confirmed using various datasets, marks a significant improvement over existing models, especially in complex and incomplete data environments.

• AVs leveraging cognitive processes for trajectory prediction.
• Teacher-student distillation technique for dynamic scenario adaptation.
• Empirical success on multiple benchmark datasets.
• Enhanced safety and operational efficiency in AV technology.

By integrating elements of human decision-making, this paper sheds light on the future of AV navigation systems, highlighting the move towards incorporating cognitive processes into machine learning algorithms for driving.