Intelligent connected vehicles (ICVs) are the development trend of the automobile industry. The Establishing the corresponding driving model according to the characteristics of ICV is necessary when designing intelligent decision and control system. This paper mainly considers the car-following behavior. First, a form of stochastic local multi-vehicle optimal velocity (SLMOV) model is proposed to characterize the car-following behavior of ICVs. The deterministic part of the model is established based on multi-vehicle information in perception and decision-making control, namely, local multi-vehicle optimal speed (LMOV) model. The stability conditions of the LMOV model are deduced via stability theory analysis. In comparison with three classical car-following models, the LMOV model has the most stable stability critical curve, that is, the stability region of the LMOV model is the largest. Meanwhile, the stability of the LMOV model is analyzed by comparing the fluctuation of vehicle speed caused by initial perturbation with time under the same conditions. Results show that the stability of the LMOV model can be improved to a certain extent by the rear-view effect and multi-front vehicle speed difference information. Second, the Wiener process is introduced to describe the random factors in traffic system, such as the road environment and vehicles. The effects of stochastic fluctuations from different sources on car-following behavior are studied via theoretical analysis and numerical simulation of stochastic stability. Results show that the influence is mainly determined by the intensity of stochastic fluctuation at a certain time. Third, the parameter estimation method is constructed according to the properties of the SLMOV model, including the deterministic part and stochastic fluctuation term. Last, the Next Generation Simulation (NGSIM) data are used to estimate the parameters of the SLMOV model and compare its results with the full velocity difference model.

CICTP