# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 1 Introduction  
Pedestrian trajectory prediction entails forecasting the potential movement paths of pedestrians over a future period based on the observed historical trajectory data. With the rapid advancement of autonomous vehicle and robotic technology, the research in this domain has gained increasing prominence. During movement, these intelligent agents must inevitably account for the impact of pedestrians in their environment. Consequently, pedestrian trajectory prediction technology serves as a crucial foundation for variousresearch fields, including autonomous driving, pedestrian surveillance, and autonomous robotic navigation [1; 2]. Despite the notable advancements in real-time recognition technology, autonomous agents still experience a temporal delay in recognition processes. This latency in decision making can be critical for other traffic participants, as ensuring pedestrian safety constitutes a fundamental ethical prerequisite for the development and deployment of autonomous mobility systems. As a result, the precise extraction of pedestrian interaction information to predict movement trajectories is of paramount importance. However, due to the highly dynamic and intrinsically interactive nature of pedestrian movement, pedestrian trajectory prediction remains a formidable challenge. Predicting the trajectories in complex and dynamic movement scenarios necessitates the most accurate possible extraction of the interaction features among moving traffic participants to achieve the robust modeling of pedestrian interactions and enhanced trajectory prediction. In the early pedestrian trajectory prediction research, traditional hand-crafted knowledge modeling techniques [3; 4] were employed to represent pedestrian interactions, yet these approaches proved inadequate in effectively capturing the latent interaction features within crowds. Subsequent studies leveraged Long Short-Term Memory (LSTM) networks, an extension of Recurrent Neural Networks (RNNs) [5; 6], to aggregate and extract the potential interaction features. These networks modeled pedestrian interactions based on positional information and focused on capturing these latent relationships through attention mechanisms [7]. Owing to its robust capability in processing temporal features, LSTM [8] has also been used in vehicle trajectory prediction studies. For instance, [PERSON] et al. [9] integrated driving intent, driving style, and historical trajectory data to model vehicles for trajectory prediction. Given the inherent strengths of Graph Neural Networks (GNNs) [10; 11] in aggregating and computing topological social interaction structures, researchers have also proposed GNN-based pedestrian trajectory prediction methods. By capitalizing on the advantages of GNNs in processing non-Euclidean data, these methods have demonstrated promising predictive capabilities. This study also employs graph-based methodologies for pedestrian trajectory prediction. In the prior research, scholars attempted to model pedestrian interactions using rectangular-based [12] or circular-based [13] approaches. [PERSON] et al. [14] introduced directionality by utilizing head orientation features. However, this approach requires pre-labeling the dataset and lacks generalizability to other datasets. Subsequently, [PERSON] et al. [15] attempted to address this limitation by calculating social attention through azimuth. Nevertheless, this method is insufficient as it relies solely on azimuth, failing to adequately capture the complex interaction characteristics between pedestrians. The interaction features considered in such scenarios do not align with the actual interaction characteristics of pedestrians in time-based motion. Moreover, the previous research insufficiently leveraged the group characteristics within pedestrian crowds. The members of a group within a crowd often exhibit behavioral consistency, a critical aspect of trajectory prediction.