Risk-aware pedestrian-vehicle interaction: A multi-objective Bayesian-optimized social force model

Accurate simulation of pedestrian–vehicle interactions is essential for urban planning, autonomous driving, and traffic safety analysis. Beyond simulation accuracy, there is a growing need for simulation frameworks that are physically grounded, interpretable, and capable of representing risk-aware decision-making processes. However, traditional physics-based microscopic models, such as Social Force Models (SFMs), often struggle to represent realistic interaction behavior due to oversimplified assumptions, static parameter tuning, and limited consideration of risk perception. To overcome these limitations, this study proposes Risk-Aware SFM for interpretable and physics-consistent behavioral simulation, which incorporates a velocity-difference-based risk perception mechanism and an adaptive interaction decision-making module. These enhancements enable the model to dynamically adjust pedestrian behavior in response to vehicle movements and contextual traffic conditions. To ensure robust performance across various traffic scenarios, a multi-objective Evolutionary Bayesian Optimization (EBO) framework is introduced to jointly calibrate key model parameters based on two complementary criteria: trajectory accuracy, measured by Average Displacement Error (ADE), and interaction realism, measured by Relative Post-Encroachment Time Error (RPETE). Extensive experiments on two real-world datasets, CITR and DUT, demonstrate that the proposed framework consistently improves both trajectory realism and behavioral fidelity compared with existing SFM variants. The proposed multi-objective calibration strategy effectively balances competing objectives while maintaining strong adaptability in both structured and unstructured environments. Overall, the proposed model provides a transparent, data-efficient, and scalable simulation framework for modeling pedestrian–vehicle interactions under risk-aware and context-sensitive conditions, offering a valuable foundation for safety-oriented traffic simulation and scenario analysis in intelligent transportation systems.