Path planning and decision-making algorithm is one of the most important research directions of driverless vehicles. However, the delays of the path planning and decision-making algorithms could lead to the inconsistency between sensor information and real driving environment, introducing negative effects on the ability to avoid dangerous state. Classified longitudinal model was established by considering the expected route, kinematics characteristics of environmental traffic participants and vehicles to determine the safety condition of vehicle. Also, a lateral safety space model was established to determine whether it was safe to change lane. Based on the safety model, combining the environmental and vehicle dynamic characteristics, an integrated algorithm of local path planning and decision-making algorithm was provided to improve the performance of the algorithm in complex dynamic environment. In the model, the influence of environmental information was represented with artificial force such as global planning gravitation, lane changing gravitation, forward obstacle repulsion and sensor occluded scenes repulsion. Gravitations represented attractive factors’ influence and repulsions represented repulsive factors’ influence of environment. Finally, co-simulations based on Carsim/Simulink was established to analyze the delay of traditional algorithm and algorithm proposed under various typical conditions. Results showed that the proposed algorithm can reduce the time-delay effect of path planning and decision-making, and provide better control for unmanned vehicle control in complex dynamic environment.