In order to explore the scientific construction method of test scenarios for the function verification of vehicle active safety technology, the high-fidelity test scenarios that accord with the real traffic conditions was constructed. Taking the Autonomous Emergency Braking (AEB) system as the research object, and taking the 6 639 road traffic accidents that AEB system functions applicable to which were selected from the accident database of National Highway Traffic Safety Administration as the research samples, the machine learning method was used to realize the scientific transformation from accident data to test scenarios. Aiming at the defects of traditional clustering algorithm, a fusion clustering algorithm based on the combination of hierarchical clustering and K-means clustering was proposed, and the clustering curve was introduced to carry out the clustering analysis of accident data samples. According to the 12 types of typical accident scenarios obtained by the clustering results, the construction of 14 types of test scenarios for the function verification of the AEB system was completed. The results show that the fusion clustering algorithm can reduces 8 iterations on average and the fluctuation of clustering result reduces by 3% on average by comparing with the traditional K-means clustering algorithm. And it realizes the scientific and accurate clustering of accident data samples and improves the efficiency of data clustering. The proposed test scenarios not only effectively cover the existing AEB test scenarios, but also will provide a strong support for the further expansion of the standard test scenarios.