# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 2 Related Work
### Social Awareness in Pedestrian Trajectory Prediction  
To accurately extract the pedestrian interaction features, the early trajectory prediction methods employed manually designed functions, as outlined in [3; 4], to model these interactions. However, these methods relied on simplistic mathematical formulations, which restricted their ability to comprehensively capture the complexities of pedestrian interactions. For instance, [PERSON] et al. [3] introduced the social force model, which employed attractive and repulsive forces to regulate pedestrian movement, thereby facilitating trajectory prediction. Similarly, [PERSON] et al. [4] integrated a Kalman filter with a maximum likelihood estimation algorithm, leveraging statistical inference to learn the individual motion parameters in a given scenario. While these approaches attempted to mathematically define pedestrian interactions, they failed to incorporate the latent interaction features, ultimately limiting the accuracy of pedestrian trajectory prediction. With the rapid advancements in deep learning, researchers have proposed various methodologies to extract latent interaction features for trajectory prediction. For example, in Long Short-Term Memory (LSTM)-based approaches [12; 24], [PERSON] et al. [12] developed the Social-LSTM model, which integrates an enhanced Recurrent Neural Network (RNN) to model pedestrian behavior over time. In generative adversarial learning-based research [25; 26; 27; 28], [PERSON] et al. [28] proposed Social-GAN, which introduced a pooling module to aggregate the interaction information between a target pedestrian and surrounding individuals. This model employed a generator, pooling module, and discriminator to generate multiple potential trajectories, ultimately selecting the most plausible one. Furthermore, in bivariate Gaussian distribution-based methods [10; 12], [PERSON] et al. [10] introduced the Sparse Graph Convolutional Network (SGCN), which utilized a sparse spatial graph to identify pedestrians with adaptive interactions and a sparse temporal graph to model pedestrian motion trends. By integrating these two sparse graphs, the model estimated the parameters of a bivariate Gaussian distribution, effectively capturing motion uncertainty and diversity. Moreover, in Conditional Variational AutoEncoder (CVAE)-based frame works [29; 30], [PERSON] et al. [29] proposed a model combining a cascaded CVAE module with a social awareness regression module. The former estimated future trajectories by linking past trajectories with predicted positions, while the latter refined trajectory predictions by generating offsets. Although these data-driven neural networks excel in learning complex interaction patterns, their extracted features lack interpretability and fail to explicitly correspond to physically meaningful attributes. To address this limitation, the subsequent research has reintroduced manually defined functions to impose feature constraints, thereby facilitating the extraction of interaction features with clear physical or domain significance. Studies in bionics and psychology suggest that such analytical approaches better reflect real-world pedestrian behavior, as human movement is typically not dictated by complex mathematical equations but rather shaped by simple prior constraints and past experiences. This concept has been effectively applied in graph-based methods. For instance, [PERSON] et al. [31] formulated pedestrian trajectory prediction as a graph structure problem, leveraging a Graph Convolutional Network (GCN) to capture the spatiotemporal dependencies after feature constraints, subsequently predicting future trajectories using a Gaussian distribution. However, pedestrian interactions are not uniform, as individuals exhibit varying degrees of influence on each other. [PERSON] et al. [17] introduced Social-TAG, which explicitly controlled the field of vision, selecting pedestrians within a predefined range for interaction calculations and employing grouping strategies.