Design of automated merging control by minimizing decision entropy of drivers on main lane

This paper presents a new control method for merging task at highway junction by using the model predictive control in which the decision entropy of the drivers on the main lane is explicitly considered as the cost function. Authors have already proposed the evaluation measure for the acceptance of the drivers on the main lane (supposed to be manual cars) to the merging car approaching from the merging lane (supposed to be automated car). In addition, the decision entropy of the driver on the main lane has been formally defined by using the stochastic model of the decision making. Based on this previous study, a new control method for the merging task of the automated car is addressed. The control problem is formulated so as to find the optimal speed of the merging car which minimizes the decision entropy of the drivers on the main lane. The proposed control strategy achieves the harmonized merging task in a sense that the drivers on the main lane can easily decide whether to accept or reject the cut-in of the merging car. The model predictive control is formulated as a nonlinear optimization problem, and solved by using the randomized approach. Finally, the validity of the proposed method is verified through some simulation studies.