## 2 Related Work
### Scene Interaction Modeling Between Pedestrians and Environment  
In the pedestrian trajectory task, since the influence of other pedestrians and objects can change or limit the pedestrian's activities, it is also necessary to pay attention to these scene factors, and thus researchers have introduced the attention mechanism into it, such as the CGNS model and the MRGL model. Among them, the CGNS model uses a soft attention mechanism to extract the scene context image information and then uses gating units to capture the historical trajectory and predict the future trajectory. Ref. [10] uses the attention mechanism and LSTM network to establish the influence of the neighboring pedestrians in a certain area of the scene and then uses the LSTM network to predict the trajectory. Ref. [13], using S-GAN, combines social and physical attention mechanisms to design the Sophie model, which focuses on both the physical environment and the trajectories of other pedestrians in the vicinity. Ref. [14] loops visual attention forcing and social forcing to focus on human-scene interactions and social interactions between pedestrians, respectively, and introduces a variant of the info variant of the GAN structure to predict trajectories with multimodal behavior. There are also articles such as [15; 16], which extract scene environment information by using LSTM or through convolutional neural networks. Ref. [17] designed three recurrent neural networks based on the Social-LSTM model to capture the human, social, and scene scale information, and improved the prediction of pedestrian trajectories by using the Social-LSTM model that improved the prediction accuracy of pedestrian trajectories. Ref. [18] proposes a wavelet transform graph convolution network model by constructing spatial and temporal graphs and then obtaining the attention score matrix through the self-attention mechanism in the temporal domain and combining it with scene features. Finally, the graph is combined with the adjacency matrix by employing graph convolution in order to obtain spatial and temporal interaction features. Similarly, ref. [19] introduces GAT to model the social interactions between pedestrians, uses VGG networks to directly extract scene information, and predicts future trajectories by combining the pedestrian social interaction information, scene information, and pedestrian motion information. In pedestrian dynamics research, pedestrians continuously adjust their trajectories under the influence of external social forces and respond differently to various environmental conditions. This results in their movement being constrained by certain rules [1]. As illustrated in Figure 1, pedestrian trajectories are influenced by both physical environmental information and the movements of other pedestrians. Existing research on pedestrian interactions primarily focuses on two factors: the distance and speed between pedestrians, with less attention given to other interaction-related information. Furthermore, nearly all studies that consider scene information rely on convolutional neural networks to compute scene features, often neglecting the analysis of historical pedestrian trajectories. In fact, historical trajectory information allows for a clearer observation of pedestrians' preferences in different areas of the scene. This understanding also helps to reveal the potential correlations between pedestrians and their environment, which is more meaningful than the standard scene information extracted by convolutional neural networks. To address the limitations of existing research, this paper makes the following innovative contributions:  
1. The interaction attention module is designed from the perspective of target pedestrians. It illustrates the influence mechanism of pedestrian interactions through four types of interaction information: repulsive force, pedestrian direction, motion direction, and speed difference.