Modeling and Analysis of Driving Behavior Based on a Probability-Weighted ARX Model

This paper proposes a probability-weighted autoregressive exogenous (PrARX) model wherein the multiple ARX models are composed of the probabilistic weighting functions. This model can represent both the motion-control and decision-making aspects of the driving behavior. As the probabilistic weighting function, a “softmax” function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. The “soft” partition defined by the PrARX model can represent the decision-making characteristics of the driver with vagueness. This vagueness can be quantified by introducing the “decision entropy.” In addition, it can be easily extended to the online estimation scheme due to its small computational cost. Finally, the proposed model is applied to the modeling of the vehicle-following task, and the usefulness of the model is verified and discussed.