A Motivational Approach to Describing Pedestrian-Vehicle Interaction Behavior Based on the Dual-Accumulator Model

This paper presents a comprehensive Pedestrian-Vehicle Interaction Behavior Model (PVIBM) grounded in the fundamental logic of human behavior, decomposing pedestrian actions into three sequential processes: perception, decision-making, and action. Each subprocess is modeled separately to achieve a holistic understanding of pedestrian-vehicle interactions. To infer vehicle intentions, a role-immersing experiment is designed to collect relevant data, which is then analyzed using machine learning techniques. Pedestrian actions are extensively modeled by adapting the dual accumulator model within a game-theoretic framework, incorporating a specifically designed utility function for pedestrian-vehicle interactions. Genetic algorithms are employed for parameter identification of the proposed PVIBM using the inD dataset [1], yielding a reasonable parameter distribution and high model accuracy. The results from case studies and simulations demonstrate that PVIBM not only offers interpretability but also produces pedestrian decision-making outcomes and speed distributions closely resembling real pedestrian behavior.