Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic vehicle motion therefore is critically important for autonomous driving simulation, for threat assessment and trajectory planning, to name a few. Traditionally the motion of traffic vehicles is considered to be deterministic and often modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic vehicle motion considering its uncertainties, based on Gaussian process (GP). A probability distribution function is employed to represent traffic vehicles’ future trajectories, which are further classified based on Gaussian Mixture Model (GMM) into typical motion trajectories. Then the GP-based motion model is built from the typical motion trajectories. With this model, any potential trajectories of traffic vehicles can be simulated by sampling the GP conditional distribution. Experiment has been performed in a high-fidelity driving simulator with a full-motion base. The results have demonstrated that the proposed GP-based model can faithfully represent the uncertainties of traffic vehicles motion, thus, is suitable to high-fidelity simulation of autonomous driving systems.