## 1 Introduction  
As intelligent interactive systems continue to advance, it is becoming increasingly critical for machines to understand human behavior and provide reliable human-machine interactions. In many applications, such as a mobile social robot [1; 2] in a crowded area, an autonomous vehicle [3; 4] navigating complicated roadways, or a mobile agent [5] in an unstructured setting, pedestrian trajectory prediction is a critical process. These machines must avoid collisions with pedestrians or other obstacles to accomplish their tasks. Hence, accurately predicting pedestrian trajectories has become an urgent requirement. Trajectory prediction is essentially a challenge involving sequence generation [6], forecasting future paths for a specific time period using observed data like previous trajectories. The future trajectory of a pedestrian, however, differs from other sequence prediction tasks in that it presents various possible future options and is strongly influenced by the surrounding people and environment. Figure 1 shows a pedestrian's future paths of movement on a sidewalk, which are influenced by other nearby pedestrians, vehicles on the road, and adjacent buildings, resulting in multiple potential routes. Hence, a wide range of influences that affect future trajectories [7] must be thoroughly taken into account, including the pedestrian's relative location, destination, spatial circumstances, and social norms. The complexity and variability of human behavior, especially in crowded situations, make it difficult to model all these aspects together. Pioneering works, such as the Social Forces Model [8], use model-based handcrafted rules to predict pedestrian trajectories and simulate interactions between people and their surroundings. Although dynamic model-based methods provide a theoretical grasp of pedestrian dynamics [9], they may struggle to capture complex and latent interactions in crowded situations. Nowadays, data-driven approaches, which can learn sophisticated interactions among pedestrians and predict future trajectories, have become increasingly popular [10]. Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM), are inherently suitable for temporal sequence predictions. Social LSTM [6], which incorporates a social pooling module to enable neighboring LSTM sequences to share their hidden information, is one of the most well-known early efforts employing an RNN to predict pedestrian trajectories, and it has become a baseline. In addition to RNNs [11; 12; 13; 14; 15; 16; 17; 18; 19], generative networks [20; 21; 22; 23; 24] are utilized to learn the distribution of observed pedestrian trajectories and predict a variety of feasible future trajectories. Regardless of which network is used, social interactions among pedestrians and spatial interactions with the environment should be considered. Social interaction modules embed different motion features of pedestrians and then aggregate them through different methods such as social pooling [6], social attention [22], graph neural network [11; 25], and direct concatenation [26]. Spatial attributes, such as obstacles and pedestrian paths, influence the pedestrian's route selection and can improve trajectory prediction accuracy through spatial semantic representation. SoPhie [22] was the first approach to unitize generative networks and jointly model social and physical attentions. Deep learning-based pedestrian trajectory methods employ social attention modules to simulate interactions among pedestrians. [PERSON] et al. [26] emphasized the importance of designing the input state for these modules. Previous works have considered different inputs, such as relative positions [6; 11; 17], relative velocities [27; 28; 29], and LSTM hidden states [22; 30; 31; 32]. Evidently, the comprehensive motion features of pedestrians are pivotal for trajectory prediction.