Social interaction capability (SIC) is essential for autonomous vehicles (AVs) when they interact with surrounding vehicles, as the ability of understanding and reacting to the behaviors of other road users can significantly enhance AVs’ rapid deployment. Virtual simulation testing is a core approach for evaluating AVs, including their SIC, on the basis of traffic simulation models. However, existing simulation models focus mainly on generating accurate vehicle trajectories and do not explicitly model the high-level sociality nature of interaction decisions that guide specific movements. This study aims to address this gap by developing a multiagent simulation model for the social interaction of human driving behavior on the basis of the multiagent imitation learning (MAIL) approach, which is referred to as the Social-MAIL model. Specifically, to quantify the sociality of decisions, we introduce social value orientation into the reward function to quantify cooperation or competition intent and guide the generation of social driving behaviors. Furthermore, to fully depict the complex interaction environment, we develop a heterogeneous policy network with temporal‒spatial attention mechanisms to describe the impact of multiple interactive objects and historical states on driving behavior. Through training and validation on the SinD dataset, we demonstrate that, compared with a set of baseline models, the proposed Social-MAIL model can accurately capture complex and time-varying social intent and reproduce the most realistic vehicle trajectories and macroscopic traffic flow characteristics at intersections. Moreover, we apply the Social-MAIL model for evaluating the SIC of AVs via comparison experiments.