Towards Equilibrium-based Interaction Modeling for Pedestrian Path Prediction

Predicting the future paths of pedestrians is shown to be crucial in various research domains, including intelligent vehicles and surveillance. Although numerous studies are conducted, the task has remained challenging, requiring a fundamental understanding of the complex human-human interactions. While current interaction-based frameworks are developed to benefit from information sharing between pedestrians, such frameworks are often ill-suited for monitoring the decisions-making process of pedestrians. In this paper, the problem of human-human interaction has been tackled by combining tools from multi-agent decision theory. Employing the game theory solution concept of Nash equilibrium, a novel equilibrium-based modification is proposed. With the aim of generalization, the proposed modification is applied to two different interaction-based frameworks and verified on publicly available datasets. Through these experiments, the advantages of the proposed modification are demonstrated.