Risk Field Model of Driving and Its Application in Modeling Car-Following Behavior

Abstract

Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation studies and can be applied to automated driving systems to provide human-like decision making. Previous modeling methods can be mainly divided into scenario-based modeling methods and field theory-based modeling methods. Scenario-based models are based on behavior theories that can explain behavioral mechanisms and field theory-based models are convenient for application to different scenarios. Combining two behavior theories and field theory, this paper aims to present a novel method to uniformly model the driving behavior in different scenarios. Risk homeostasis theory and preview-follower theory are used as the theoretical foundation, and field theory is utilized to connect the two behavior theories. A new risk field model is constructed for better coupling these behavior theories. Integrating these theories, this study then develops a subjectively perceived risk quantification method and a trajectory and motion planning model, which are validated using naturalistic data in car-following scenarios. Results show the effectiveness of this method and this model with reference to an effective risk quantification index (safety margin) and in comparison with the classical models (desired safety margin model and intelligent driver model) using naturalistic data in car-following scenarios.