Multi Agent Trajectory Prediction

Multi-agent trajectory prediction aims to forecast the future movements of multiple interacting agents, such as vehicles or pedestrians, a crucial task for autonomous systems and robotics. Current research heavily utilizes deep learning, focusing on graph neural networks, transformers, and variations like hypergraph transformers, to model complex spatial and temporal interactions between agents and their environment, often incorporating multi-source data like sensor readings and maps. These advancements improve prediction accuracy and efficiency, leading to safer and more effective autonomous navigation and decision-making in dynamic environments.