For the problems that the estimation accuracy decreases or even diverges due to the strong nonlinearity of the model and the non-Gaussian distribution of noise in vehicle state estimation, this paper proposes a vehicle state estimation algorithm based on adaptive cubature particle filtering (ACPF). Firstly, a high-dimensional non-linear eight DOF (Degree-of-freedom) vehicle model is constructed based on the unsteady dynamic tire model. Secondly, the importance density function of the basic particle filtering (PF) is updated by the adaptive cubature Kalman filtering (ACKF) algorithm to achieve the design of the adaptive cubature particle filter algorithm. Based on vehicle sensor information and ACPF algorithm, an accurate on-line observation of key state variables such as roll angle and side slip angle is realized. Simulink-Carsim joint simulation platform is built to verify the algorithm. The results show that the state estimation accuracy of the algorithm is higher than that of the traditional unscented particle filtering (UPF) algorithm, the operation efficiency of the algorithm is higher than that of UPF algorithm, the traditional particle filter algorithm estimates divergence. The research results may provide theoretical support for accurate control of vehicle dynamics.