Identifying typical pre-crash scenarios can assist in determining potentially dangerous road traffic situations, and provide a basis for further expansions to vehicle safety test scenarios. Firstly, for the purpose of identifying the typical pre-crash scenarios of road traffic in China, 5983 accident cases in the China Traffic Accident In-depth Study (CIDAS) Database were screened. Next, the following variables have been identified as characteristic variables of scenario identification: personnel injury, the type of road, the form of accident, accident time, the cause of the accident (including human factors, vehicle factors, and environmental factors), and the casualties in accident. Then, the correlation analysis was conducted using the Pearson correlation coefficient for the selected variables. After that, the SSE (sum of squared errors) index was used to determine the number of cluster center. Finally, we described five typical Chinese road traffic pre-crash scenarios utilizing the K-means++ clustering method as well as the vehicle motion state, driver behavior, and road environment parameters in each of the five scenarios. In the following five scenarios, the type of accident (UTYP) in CIDAS and the most significant accident inducements were examined. The classification of pre-crash scenarios can help researchers determine the primary area of investigations, identify opportunities for human intervention, determine the effectiveness of selected collision countermeasures, and it may be possible to expand the scope of vehicle safety tests.