Probabilistic Modeling of Human Driver's Natural Behavior Distribution: An Ensemble Learning and GMM Framework for Car-Following Model

Probabilistic modeling of the natural distribution of human driving behavior is crucial for constructing realistic and dynamic traffic simulation environments. To capture the complex, high-dimensional, and multi-modal characteristics of driving behavior, this paper proposes an ensemble-based probabilistic modeling framework. A Bayesian Gaussian Mixture Model (GMM) is employed as a base predictor to model the multi-modal distribution, while ensemble learning is utilized to reduce model bias and improve robustness. To address challenges associated with high-dimensional features and limited sample sizes, dimensionality reduction and weighted bootstrap sampling are introduced to construct diverse meta models. An error evaluation network is developed to assess prediction quality in terms of mean, variance, and reliability. To handle distributional shifts across scenarios, an adaptive meta model with online learning capability is proposed for dynamic parameter updating and improved transferability. Furthermore, a reliability–uncertainty fusion strategy inspired by mean–variance theory is designed to dynamically assign optimal weights to individual meta models. Experiments on three datasets—HighD, SPMD, and MTBD—demonstrate superior performance in accuracy, robustness, and generalization.