# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 1 Introduction  
Pedestrian trajectory prediction (_PTP_) is a critical task in the fields of computer vision [1, 2, 3] and autonomous driving [4], marked by significant challenges. The core of this research extends beyond understanding the individual motion characteristics of pedestrians, also known as agents: it necessitates a thorough investigation into the complex interactions among agents as well as between agents and their environment. As illustrated in Figure 1, using the blue agent as an example, the interactions among agents and between agents and the environment are depicted. These interactions are not only multimodal but also highly dynamic, influenced by human self-awareness and social behavioral norms. Thus, predicting the motion trajectories of multiple agents requires modeling or considering two key factors: temporal aspects and social dynamics. Temporally, the historical state of each agent impacts its future state. It is essential to model the temporal dependencies of each agent's past state to predict their future trajectories. Socially, the states of surrounding agents and environmental information continuously influence the decision-making of a central agent. Therefore, modeling the interactions among agents and the complexities of the surrounding environment is necessary to capture intricate social dynamics. Early models for crowd trajectory prediction primarily relied on manually designed energy functions to simulate interactions among individuals. This approach necessitates extensive feature engineering and is limited in capturing pedestrian interactions in busy scenes. However, the advancement of deep neural network technologies, particularly the application of Recurrent Neural Networks (RNNs) in pedestrian trajectory prediction, offers new perspectives to address this challenge. RNN-based models [1, 2], by analyzing the hidden states of pedestrians, capture their motion trajectories and simulate interactions among individuals by integrating the hidden states of surrounding pedestrians. This approach has somewhat improved the understanding of social behavioral dynamics. As a complement to RNN models, social-pooling [1, 2] treats the states of neighboring pedestrians equitably and uses pooling techniques to integrate these states, thereby considering the spatial relationships between agents. In contrast, attention mechanisms [5, 6], as advanced models, differentiate the importance of nearby pedestrians through learned weighting functions, providing a more flexible approach to trajectory prediction. Despite these advances, existing attention mechanisms still exhibit limitations in fully simulating the complex interactions among agents and between agents and their environment [7]. The traditional attention mechanism faces challenges in handling complex interactions among multiple pedestrians. In scenarios such as intersections, agents may exhibit various interaction modes, such as avoiding and merging. Traditional attention mechanisms struggle to adapt flexibly to these variations, resulting in a suboptimal performance in capturing relationships between multiple agents. Furthermore, the conventional single-head attention mechanism is inadequate in processing multi-scale inputs. In pedestrian  
Figure 1: Example of pedestrian motion trajectories in a real-world scenario. Pedestrian trajectory prediction should consider information such as interactions among agents and with the scene environment. trajectory prediction, agent movements may involve different spatio-temporal scales. The single-head structure of traditional mechanisms cannot simultaneously focus on and adapt to these varying scale features, thereby limiting the model's capacity to handle multi-scale information. On the other hand, in many traditional models, the environment is treated as a static backdrop or mere obstacles, without an in-depth consideration of its complex semantic information, such as road signs and traffic signals.