Dependent Hidden Markov Model for pedestrian intention prediction: considering Multivariate Interaction Force

Abstract

Accurately recognizing and predicting pedestrian intentions is crucial for autonomous vehicle safety. However, existing prediction models often fail to comprehensively consider interactions between various traffic elements, resulting in suboptimal accuracy and robustness, especially in complex environments. To address this, we propose a pedestrian intention prediction model combining the Multivariate Interaction Force (MIF) model and a Dependent Hidden Markov Model (DE-HMM) for unsignalized midblock crossings. The MIF model captures dynamic interactions among pedestrians, vehicles, and the environment, while DE-HMM uses MIF data and pedestrian head orientation for predictions. Our model achieves 91.5% accuracy in recognizing crossing intentions, and 88.7% and 85.1% accuracy for predictions 0.5s and 1s ahead, respectively, outperforming current mainstream models and demonstrating strong robustness in special scenarios.