With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) will become a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it’s also crucial to consider collaboration between AVs and HVs. Human-like interaction is a required capability for AVs, especially at common unsignalized intersections, as human drivers of HVs expect to maintain their driving habits for intervehicle interactions. This article uses the social value orientation (SVO) in the decision making of vehicles to describe the social interaction among multiple vehicles. Specifically, we define the quantitative calculation of the conflict-involved SVO at unsignalized intersections to enhance decision making based on the reinforcement learning method. We use naturalistic driving scenarios with highly interactive motions for the performance evaluation of the proposed method. The experimental results show that SVO is more effective in characterizing intervehicle interactions than conventional motion-state parameters like velocity, and the proposed method can accurately reproduce naturalistic driving trajectories compared to behavior cloning.