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：在自动驾驶汽车这一新兴领域，轨迹预测仍是一项重大挑战，尤其是在混合交通环境中。传统方法通常依赖于时间序列分析等计算方法。我们的研究则另辟蹊径，采用跨学科方法，将人类认知和观察行为原理融入自动驾驶轨迹预测模型中。我们提出了一种新颖的"自适应视觉扇形"机制，该机制模仿人类驾驶员基于空间方位、距离和行驶速度等因素的动态注意力分配。同时，利用卷积神经网络和图注意力网络构建"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表明，我们的模型（GAVA）分别以至少15.2%、19.4%和12.0%的优势超越当前最先进基线模型。研究结果揭示了利用人类认知原理提升自动驾驶轨迹预测算法效能与自适应能力的潜力。所提模型的代码已发布于我们的Github。