This study aims to define interaction criteria between vehicles and incorporate them into an autonomous vehicle lane-changing path planning algorithm to minimize the differences in driving behavior between human-driven vehicles and autonomous vehicles. Traditional autonomous vehicle path planning research has mainly focused on physical safety distances and collision avoidance, but this approach does not fully reflect the complex interactions between vehicles in real road environments. Human-driven vehicles continuously interact with surrounding vehicles while changing lanes, necessitating the exploration of autonomous driving path planning that accounts for these interactions. This study focuses on mandatory lane-changing scenarios that require active interaction with surrounding vehicles. For modeling the criteria of interaction, an experimental environment was designed where two driving simulators influence each other while changing lanes. Using data obtained from the experiments, the merging probability is modeled with logistic regression and applied to the cost function of an optimal polynomial path generation algorithm. By involving human drivers in the simulators the proposed model and the baseline one were compared. The proposed model demonstrated, compared to the baseline model, a higher lane change success rate, less speed reduction in human-driven vehicles, and shorter lane change times for autonomous vehicles. This suggests that the proposed model can effectively reduce the heterogeneous driving behaviors between human-driven and autonomous vehicles. The results validate the effectiveness of integrating vehicle interactions into lane-changing path planning.