【摘要】 在正确地规划合理路径方面，行人轨迹预测具有重要的意义.大多数现有轨迹预测方法在考虑周围行人的影响时，都是简单地将周围行人全部考虑在内，这必然带来的冗余信息.本文提出了一种基于多模式时空交互的行人轨迹预测模型，该模型通过多模式行人空间交互模块对不同行人在不同情况下给予不同的权重，使得模型可以有效减小冗余信息带来的影响.并且本文的模型针对于输入轨迹信息的不同重要程度，设计了加权信息融合模块在原轨迹信息的基础上融合了赋予不同权重的历史轨迹信息，使得模型的轨迹信息更加有效.此外，该模型采用了时间卷积网络模块来捕获行人的时间交互.实验结果表明，在公开数据集ETH和UCY上，相比于Social-STGCNN平均位移误差(Average Displacement Error, ADE)和终点位移误差(Final Displacement Error, FDE)分别降低了15%和14%.更多还原

【Abstract】 Pedestrian trajectory prediction plays an important role in correctly planning reasonable paths. Most of the existing trajectory prediction methods simply take all the pedestrians into account when considering the influence of the surrounding pedestrians, which inevitably brings redundant information. A pedestrian trajectory prediction model based on multi-mode space-time interaction is proposed. This model gives different weights to different pedestrians in different situations through multi-mode pedestrian space interaction module, which makes the model effectively reduce the impact of redundant information. Aiming at the different importance of the input trajectory information, the weighted information fusion module is designed to integrate the historical trajectory information with different weights on the basis of the original trajectory information, so as to make the trajectory information of the model more effective. In addition, the model uses time convolution network module to capture pedestrian time interaction. The experimental results show that compared with social-stgcnn, average displacement error(ADE) and final displacement error(FDE) is reduced by 15% and 14% respectively on the open data sets ETH and UCY.更多还原