Department of Transportation Engineering, Ajou University, Suwon 16499, Korea.
Sensors (Basel). 2021 Oct 19;21(20):6929. doi: 10.3390/s21206929.
As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.
近年来，随着自动驾驶汽车的研究和开发活动日益活跃，开发测试场景和方法已成为评估和确保其安全性的必要手段。基于当前的背景，本研究使用交通事故数据和自然语言处理技术开发了一种自动驾驶汽车测试场景推导方法。基于自然语言处理技术的测试场景挖掘方法为城市干道生成了 16 个功能测试场景，为城市交叉口生成了 38 个场景。通过确定可以用生成的测试场景解释的交通事故记录数量来验证所提出的方法。也就是说，生成的测试场景是有效的，并且代表了测试场景与增加的交通事故记录之间的匹配率。所提出的方法生成的功能场景分别占城市干道和交叉口场景实际交通事故的 43.69%和 27.63%。
