高速公路拥堵工况下自动驾驶车辆切入场景测试集构建与安全性评估

Construction Test Set and Risk Assessment of Cut-in Scenarios for Autonomous Vehicles under Highway Congestion Conditions

为评估中国高速公路拥堵工况下自动驾驶汽车在切入场景中的安全风险,从自然驾驶数据集中提取64个切入样本,采用六层次模型和相关性分析方法确定场景要素范围,通过抽样生成1 000个测试用例并构建安全评估指标体系分析车辆运行安全,运用随机森林算法确定引发风险的关键因素。结果表明:在1 000个测试用例中,风险场景占比5.3%,纵向相对速度是导致风险的关键要素;拥堵工况下,环境车辆速度低于自动驾驶车辆速度23%时形成高风险切入场景,该指标可作为拥堵切入场景下自动驾驶汽车识别风险的预测指标,亦可用于该场景下的事故责任认定。

To assess the safety risks of autonomous vehicles during cut-in scenarios on congested Chinese highways,64 cut-in samples were extracted from a natural driving dataset.Employing a six-level model and correlation analysis,the static and dynamic factors of the scenarios were defined.Subsequently,1000 test cases were randomly generated through sampling,and a safety assessment index system was established to analyze the safety of vehicle operations.Lastly,the random forest algorithm was applied to identify the key factors triggering risks.Results indicate that risk scenarios account for 5.3%of the total,with longitudinal relative velocity identified as the crucial factor.Under congested conditions,a high-risk cut-in scenario is formed when the speed of surrounding vehicles is 23%lower than that of autonomous vehicles,this indicator serves as a crucial predictive measure for identifying collision risks in congested cut-in scenarios for autonomous vehicles and may be applied in determining liability of accident in such scenarios.