提出一种结合群体交互信息和个体运动信息的生成对抗网络GI-GAN。首先,利用编码层中的双向长短期记忆网络BiLSTM提取观测时段内所有行人自身的运动行为隐藏特征;其次,基于双注意力模块,计算与轨迹生成关联度较高的个体运动信息和群体交互信息;最后,利用生成对抗网络进行全局联合训练,获得反向传播误差和各层的合理网络参数,解码器利用已获取的上下文信息生成多条合理预测轨迹。实验表明,与S-GAN模型相比,GI-GAN模型的平均位移误差和绝对位移误差分别降低了8.8%和9.2%,并且预测轨迹具有更高的精度和合理多样性。

In this paper, a generative adversarial network GI-GAN that combines group interaction information with individual motion information is proposed. First, BiLSTM in the coding layer was used to extract the movement behavior of all pedestrians during the observation period. Second, based on a dual attention module, individual motion information and group interaction information having a high correlation with trajectory generation were calculated. Finally, using the generative adversarial network structure, global joint training was performed and the backpropagation error was obtained. Then, reasonable network parameters for each layer were obtained. Subsequently, the decoder used the acquired context information to generate multiple reasonable prediction trajectories. Experiment results show that compared with the S-GAN model, the average displacement error and absolute displacement error of the GI-GAN model are reduced by 8.8% and 9.2%, respectively, and the predicted trajectories have a higher accuracy and reasonable diversity.