Quantitative Driver Acceptance Modeling for Merging Car at Highway Junction and Its Application to the Design of Merging Behavior Control

This study models the decision-making characteristics of a driver regarding whether he accepts a merging car at a highway junction. Then, the application of the modeling to the design of merging behavior control is proposed. First, the driving behavior on the main lane at a highway junction is observed using a driving simulator, particularly focusing on the driver's state of decision (SOD), which represents the acceptance for merging a car coming from the merging lane. Second, the driver's SOD is modeled using a logistic regression model and the prediction performance of the identified model is verified. Finally, the speed controller of the merging car is designed to maximize the acceptance from the cars on the main lane. The key idea here is to minimize the entropy of the SOD of the driver on the main lane by optimizing the speed of a merging vehicle. This problem is quantitatively formulated using an identified decision-making model and addressed by applying a randomized approach to the optimization. This enables the automated vehicle to realize a considerate merging behavior at a highway junction. Numerical experiments are performed to demonstrate the usefulness of the proposed design scheme.