Abstract: Simulation is an invaluable tool in the field of autonomous driving, especially in verifying the decision-making and planning algorithms. Real vehicle experiments can potentially cause safety accidents. In highly interactive driving scenarios, it is essential to verify whether autonomous driving vehicles intelligently interact with other traffic participants. In autonomous driving simulations, the environment vehicles’ motion model is crucial, and its intelligence directly affects the driving decisions of the test autonomous vehicles, which in turn determines the evaluation of the decision-making algorithm. The current research does not focus on modeling the motion of the environment vehicles, resulting in inaccurate evaluation results between the simulation and on-field experiment, where the real-world human drivers are more intelligent. This paper proposes a novel car-following model that considers the social preferences and predictive capabilities of real drivers based on the classical intelligent driver model. This model can adapt to social preferences after predicting the future trajectories of surrounding vehicles. When surrounding vehicles intend to change lanes, the social predictive intelligent driver model (SPIDM) can decelerate in advance, thereby enhancing both driving safety and comfort. In addition, real-world data are utilized to calibrate the SPIDM by extracting the car-following preferences of real drivers under the cut-in scenarios. Different categories of social preferences are obtained to generate diverse car-following behavior. Overall, SPIDM improves the intelligence level of environment vehicles, and creates more realistic traffic environment for the autonomous driving simulation.