Social Force

Social force models (SFMs) simulate pedestrian and agent movement by representing interactions as forces, aiming to predict and understand collective behavior in crowded environments. Current research focuses on enhancing SFMs to account for interactions with robots, incorporating group dynamics, and integrating them with machine learning techniques like neural networks and reinforcement learning to improve prediction accuracy and realism, particularly in complex scenarios like shared spaces and emergency evacuations. These advancements have significant implications for improving the safety and efficiency of autonomous systems, urban planning, and crowd management in various applications.