Due to the differences in traffic situations and traffic safety laws, standards for extraction of critical driving scenarios (CDSs) vary from different countries and areas around the world. To maintain the characteristic variables under the Chinese typical CDSs, this paper uses the three-layer detection method to extract and detect CDSs in the Natural Driving Data from China-FOT project which executing under the real traffic situation in China. The first layer of detection is mainly based on the feature distributions which deviate from normal driving situations. These distributions associated with speed and longitudinal acceleration/lateral acceleration/yaw rate also quantify the critical levels classification. The second layer of detection based on the rate of brake pressure (Pressure peak/Time difference) and the relevant variables to TTC’s trigger, Pressure peak means the maximum value on brake pressure curve, Time difference means the difference between Pressure peak time and Hard breaking time (Time when driver starts to make emergency brake). The second layer could make corrections to the critical levels. The third layer of detection uses fuzzy comprehensive evaluation method to detect CDSs and make quantification of critical levels. The results show the accuracy (ACC) of detection under three-layer method makes greater optimization compared to other methods which analyze single variable. After the first two layer detections ACC achieves 69.71% while after the third layer detection ACC achieves 85.10%, 780 CDSs are extracted from these data. The results of this paper could provide a basis for the classification of CDSs from Natural Driving Data in China and causation mechanism of CDSs.