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.

基于信号化交叉口复杂场景下的车辆轨迹预测，提出了一种融合交通信号信息和多车辆交互的知识增强生成对抗网络（Knowledge-Informed Generative Adversarial Network，KI-GAN）模型，并采用专用的注意力汇聚方法，在6秒观测和预测周期内实现了平均位移误差（Average Displacement Error，ADE）为0.05和终点位移误差（Final Displacement Error，FDE）为0.12；当预测窗口扩展到9秒时，ADE和FDE值进一步降低至0.11和0.26，验证了该KI-GAN模型在复杂场景中车辆轨迹预测的有效性。

基于知识的生成对抗网络在信号灯交叉口多车轨迹预测中的应用