To meet the need of mass testing scenarios and high-risk scenarios for the autonomous vehicles safety testing and verification, and based on the accident data of 641 cases involving road section in the National Automobile Accident In-Depth Investigation System, five scene elements were selected according to traffic environment elements and test vehicle basic information elements. Then the vehicle accident data was analyzed by one-hot coding and cluster analysis methods. The dangerous accident characteristics were identified and analyzed by combining the vehicle accident data with the typical vehicle collision dangerous scenarios obtained by clustering. And 15 test scenarios of autonomous vehicles involving road section type were extracted, including 6 test scenarios involving common sections and 9 test scenarios involving intersections. Research shows that Chinese traffic environment has unique characteristics. In the test scenario, 53.3% of the target vehicles involved powered two-wheeler (including motorcycles and electric mopeds) and 40.0% involved M1 passenger vehicles. The proposed dangerous accident characteristics can better describe and clarify the test scenario.The research results can provide a test scenario with Chinese traffic environment characteristics for virtual testing of autonomous cars and a basis for the development and testing of vehicle active safety products.