In order to effectively improve the vehicle driving safety, Lane Keeping Assist System (LKAS) arises as one of the most important components of the vehicle safety assist driving system. Considering the humanmachine conflict in the vehicle control weight allocation of the auxiliary control system, in this paper, the decisionmaking of vehicle status judgment and the auxiliary control strategy are studied. Firstly, the vehicleroad reference model is established. Then, the monitoring strategy of vehicle status is proposed based on the preview theory. Furthermore, the decisionmaking rule of vehicle deviation is designed according to the torque applied on the steering wheel. Furthermore, the selflearning function of the cerebellar model articulation controller (CMAC) neural network is applied to adjust PID parameters in real time, which aims to realize the active control of vehicle steering system. Finally, the simulation is carried out by CarSim and Matlab. Simulation results illustrate that, compared with the traditional PID control, the CMACPIDcombined control algorithm can correctly recognize the state of driver operation and vehicle status. Once the vehicle deviates, the proposed algorithm can effectively generate early warning and output active deviation correction control. Simulation results demonstrate that the proposed control algorithm achieves advantages of high accuracy and fast response speed, thus ensure the driving safety and reduce the humancomputer conflict.