Pedestrian modeling with realistic dynamic behaviors and its application in virtual safety testing for autonomous vehicles

Abstract

Pedestrians, as vulnerable road users, face significant safety risks in traffic environments. In hazardous traffic scenarios, they often exhibit collision-avoidance behaviors that significantly influence vehicle interactions and potential injury risks. However, the inherent uncertainty and complexity of influencing factors make accurate modeling of pedestrian behavior in safety–critical scenarios particularly challenging. To address this, we propose a comprehensive model for pedestrian dynamic behaviors in such scenarios, which is subsequently implemented in a virtual vehicle safety testing platform to enable realistic pedestrian-vehicle interactions. The developed model systematically captures the process of pedestrian risk perception, avoidance decision-making, and kinematic response, ensuring high behavioral fidelity by leveraging real pedestrian data collected in a virtual reality experiment. Utilizing actual accident data, the constructed platform is capable of generating imminent yet realistic pedestrian-vehicle pre-crash scenarios. Furthermore, it integrates pedestrian injury risk into a unified safety evaluation framework that synergizes active and passive safety assessments. Validation with real-world data demonstrates the effectiveness of the proposed pedestrian model and testing platform. Across 4,200 simulated pedestrian-vehicle interactions, pedestrians’ active avoidance of oncoming vehicles reduced collisions by 27.77 % relative to a no-avoidance baseline, highlighting the necessity of incorporating dynamic behavior modeling into virtual testing. The virtual testing framework proposed herein enables practical implementation of the high-fidelity pedestrian model, offering a pathway for autonomous vehicles to better interpret pedestrian behaviors and enhance interactive safety.