Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of traffic signal controls, and interactions among different types of road users. To address these issues, we present in this paper a novel model called Knowledge-Informed Generative Adversarial Network (KI-GAN), which integrates both traffic signal information and multi-vehicle interactions to predict vehicle trajectories accurately. Additionally, we propose a specialized attention pooling method that accounts for vehicle orientation and proximity at intersections. Based on the SinD dataset, our KI-GAN model is able to achieve an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error (FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When the prediction window is extended to 9 seconds, the ADE and FDE values are further reduced to 0.11 and 0.26, respectively. These results demonstrate the effectiveness of the proposed KI-GAN model in vehicle trajectory prediction under complex scenarios at signalized intersections, which represents a significant advancement in the target field.

翻译：信号交叉口车辆轨迹的可靠预测对于城市交通管理与自动驾驶系统至关重要。然而，由于交叉口复杂的道路布局、交通信号控制的参与以及不同道路使用者之间的交互作用，这一任务面临独特挑战。为解决上述问题，本文提出一种名为知识融合生成对抗网络（KI-GAN）的新型模型，该模型整合交通信号信息与多车交互以精确预测车辆轨迹。此外，我们提出一种专门化的注意力池化方法，该方法考虑了交叉口处车辆的朝向与邻近性。基于SinD数据集，我们的KI-GAN模型在6秒观测与6秒预测周期下实现了平均位移误差（ADE）0.05和最终位移误差（FDE）0.12。当预测窗口延长至9秒时，ADE与FDE值分别进一步降低至0.11和0.26。这些结果验证了所提KI-GAN模型在信号交叉口复杂场景下车辆轨迹预测的有效性，标志着目标领域的一项重要进展。