Conducting a quantize value of driving risk is crucial for the human-like driving decision of intelligent vehicles (IV). Aiming at the challenge of quantifying driving risks in complex multi-task scenarios, a method of driving risk quantification of IV based on human risk perceived mechanism is proposed. By utilizing vehicle or road sensors, measurements of the surrounding environment and state information have been obtained. And a cost map of the driving scene is created by assigning costs to potential collision factors such as roads, plants, and obstacles that the driver believes may occur in the first stage. Based on the fundamental principles of human driving and vehicle motion states, a dynamic risk model is established utilizing Gaussian functions. The real-time computation of driving risk with human-like characteristics is accomplished through the integration of the cost map for the driving environment and a dynamic risk model. The simulation results demonstrate the effectiveness of the proposed method in quantifying dynamic driving risk based on human driving experience, which is applicable to autonomous driving decision-making for IV and capable of generating human-like driving behavior.