Modeling bounded rationality in pedestrian-vehicle interactions at non-signalized crosswalks: A game theoretic...

Modeling pedestrian-vehicle interactions (PVIs) is essential for automated driving systems in mixed traffic environments but challenging due to the stochasticity and complexity of human behaviors. Conventional PVI models rely on strong assumptions like perfect information and unbounded rationality. For more realistic modeling of PVIs at unsignalized crosswalks, we propose a two-agent non-zero-sum non-cooperative dynamic game incorporating Quantal Response Equilibrium. This Game Theoretic Quantal Response Equilibrium (GT-QRE) approach accommodates human agents' limited cognition and bounded rationality, while accounting for unobserved heterogeneity in road users' rationality and sequential decision-making during crossing activities. We then develop an integrated simulation platform that links pedestrians' and vehicles' decisions with their movements to simulate their interactions, where GT-QRE is used to capture the decision-making behaviors of both pedestrians and vehicles, Social Force Model (SFM) and Model Predictive Control (MPC) are used to simulate pedestrian and vehicle motions. The proposed method is calibrated and validated using empirical data at four selected crosswalks in three cities in China. The key findings include: (i) both pedestrian and vehicle exhibit limited rationality in PVI; (ii) compared to conventional Nash Equilibrium (NE), our method improves prediction of crossing decisions by 13.0% for pedestrians and 36.1% for vehicles, highlighting PVIs decision process are inherently dynamic rather than static;(iii) the GT-QRE approach effectively captures the dynamic decision-making behavior of pedestrians and vehicles during interactions; (iv) the simulation platform, which integrates GT-QRE decision-making with SFM/MPC motion models, realistically reproduces vehicle and pedestrian movements at crosswalks, yielding low longitudinal/lateral displacement and velocity errors.