  In the burgeoning field of autonomous vehicles (AVs), trajectory prediction
remains a formidable challenge, especially in mixed autonomy environments.
Traditional approaches often rely on computational methods such as time-series
analysis. Our research diverges significantly by adopting an interdisciplinary
approach that integrates principles of human cognition and observational
behavior into trajectory prediction models for AVs. We introduce a novel
"adaptive visual sector" mechanism that mimics the dynamic allocation of
attention human drivers exhibit based on factors like spatial orientation,
proximity, and driving speed. Additionally, we develop a "dynamic traffic
graph" using Convolutional Neural Networks (CNN) and Graph Attention Networks
(GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on
the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms
state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively.
Our findings underscore the potential of leveraging human cognition principles
to enhance the proficiency and adaptability of trajectory prediction algorithms
in AVs. The code for the proposed model is available at our Github.
