针对自动驾驶汽车安全性测试验证中海量测试场景以及高风险测试场景的需要，基于国家车辆事故深度调查体系中的641例事故数据，根据交通环境要素和测试车辆基础信息选取了5个场景要素，通过独热编码和聚类分析方法对车辆交通事故数据进行了分析，同时结合聚类得到的典型车辆碰撞危险场景提出并分析了危险事故特征，构建了15个涉及道路路段类型的自动驾驶测试场景，包括6个测试场景涉及普通路段、9个测试场景涉及路口路段。结果表明：中国的交通环境具有独特的特征，测试场景中目标车的53.3%涉及动力两轮车PTW（包括摩托车和电动助力车），40.0%涉及M1类乘用车；提出的危险事故特征能够更好地描述和明确测试场景。研究结果可为自动驾驶汽车的虚拟测试提供具有中国交通环境特征的测试场景，为车辆主动安全产品的开发测试提供依据。

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.