Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module including this data-driven mechanism is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance.

翻译：在人群场景中预测行人轨迹对自动驾驶或自主移动机器人领域至关重要，因为估计周围行人的未来位置有助于制定避免碰撞的决策。这是一项具有挑战性的问题，因为人类具有不同的行走动作，且当前环境中人与物体之间（尤其是人与人之间）的交互十分复杂。以往研究者专注于建模人与人交互，却忽略了交互的相对重要性。为解决该问题，本文提出一种基于相关熵的新型机制。该机制不仅能度量人与人交互的相对重要性，还能为每个行人建立个人空间。进一步地，本文提出了包含该数据驱动机制的交互模块。在该模块中，数据驱动机制可有效提取场景中动态人与人交互的特征表示，并计算对应权重以表征不同交互的重要性。为了在行人之间共享这些社交信息，本文设计了一种基于长短期记忆网络的交互感知轨迹预测架构。在两个公开数据集上的实验结果表明，与几种最新方法相比，本模型在保持良好性能的同时取得了更优效果。