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.

翻译：在自动驾驶汽车快速发展的领域中，轨迹预测仍是一项严峻挑战，尤其在混合自主环境中。传统方法通常依赖时间序列分析等计算手段。我们的研究采用跨学科方法，将人类认知与观察行为原理整合到自动驾驶轨迹预测模型中，实现了显著突破。我们提出了一种新颖的"自适应视觉区域"机制，该机制模拟人类驾驶员基于空间方位、距离和行驶速度等因素表现出的动态注意力分配特性。同时，我们利用卷积神经网络(CNN)和图注意力网络(GAT)构建"动态交通图"，以捕捉智能体间的时空依赖性。在NGSIM、HighD和MoCAD数据集上的基准测试显示，我们的模型(GAVA)分别超越现有最优基线方法至少15.2%、19.4%和12.0%。研究结果凸显了借助人类认知原理提升自动驾驶轨迹预测算法性能与适应性的潜力。所提模型代码已发布于Github。