# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 1 Introduction
This information is crucial for understanding and predicting pedestrian behavior patterns in specific environments. For instance, in urban street scenes, pedestrians' choices of crossing locations are often influenced by the presence or absence of crosswalks. They tend to safely cross at locations with traffic signals, rather than riskily traversing streets with dense vehicle flow. Effectively integrating spatial information with pedestrian interaction data remains an open question. In response to these challenges, this study introduces an innovative deep learning model _M2 Tames_ (multi-head mask temporal attention motion, interaction, semantic context) to improve the accuracy and adaptability of trajectory prediction. The new model encompasses not only the motion characteristics of agents but also the interactive features among agents as well as between agents and their environment. By employing an innovative multi-head mask temporal attention mechanism _M2 Tea_, _M2 Tames_ is capable of simultaneously focusing on the agent's motion characteristics and the interactive features between agents. Furthermore, _I2_ allows the model to capture complex social behavioral patterns among agents, such as avoidance, following, and congregating. A deep understanding of these interaction modes significantly supports prediction accuracy. Lastly, through the integration of the U-Net architecture, _M2 Tames_ learns and interprets scene semantic context, thereby deepening the understanding of spatial relationships between agents and their environment. In this context, our research aims to further enhance the accuracy of pedestrian trajectory prediction. Compared to Agentformer [5], our proposed model, _M2 Tames_, not only considers the agents' historical trajectories but also incorporates semantic information about the environment. The introduced _M2 Tea_ module employs advanced multi-scale feature processing strategies to address the shortcomings of previous works that focused solely on interactions among agents or solely between agents and the environment. Moreover, an improved interaction module, _I2_, has been developed. Unlike previous models [1; 2; 6; 7], this module emphasizes the importance of considering the movement patterns of nearby agents for accurately predicting pedestrian trajectories. This model is more effective in learning and adapting to complex interactions between agents and their environments, especially in densely populated scenarios. The contributions of this paper are as follows:  
1. This paper introduces a deep learning model _M2 Tames_ for pedestrian trajectory prediction. The model comprehensively considers the motion characteristics of agents and the interactions among themselves, or with their environment, thereby improving the accuracy and adaptability of trajectory prediction;
2. _M2 Tea_ designs a multi-head mask temporal attention mechanism that deeply characterizes temporal and interactive features, extracting the dynamic interaction features of pedestrians to enhance the model's ability to learn and simulate real-world scenarios. Additionally, _I2_ is designed to infer specific interactive relationships between agents. The goal of _I2_ is to deduce the interactive relationships between agents based on the input motion data;
3. Extensive experiments are conducted on the proposed _M2 Tames_ using the publicly available real-world datasets from SDD and ETH/UCY, and the results demonstrate that _M2 Tames_ achieves high accuracy in predicting pedestrian trajectories in both long-term and short-term settings.