智能汽车测试工况与用户工况关联评价模型

A Model for Evaluating Correlation between Test Condition and User Condition of Intelligent Vehicle

为研究智能汽车从用户工况到试验场测试工况有效性定量评价的问题,基于行车风险场理论,综合考虑加速系数、危险度覆盖率、最大危险度及危险度分布4个评价因子,提出了智能汽车测试工况与用户工况关联匹配模型和评价模型。关联匹配模型通过"危险度相等"建立了测试工况与用户工况内在的理论关联关系,提出了危险度-工况次数分布尽量一致的原则。评价模型通过"有效性指数"建立了测试工况好坏的定量评价指标。提出了加速系数越大越好、样本危险度覆盖率越大越好、最大危险度越接近用户极限值越好、危险度分布相似性越高越好的4个原则。以车辆跟随场景作为算例对关联评价模型进行验证,3种测试工况的危险度均接近用户危险度,符合匹配模型的要求。计算3种测试工况的有效性指数并按照大小进行了排序,当改变4个评价因子的权重分配时,排序发生了变化。研究结果表明,关联匹配模型能够用于对智能汽车测试工况的有效性进行定量评价,评价因子权重分配会影响有效性指数,可以按需设定。

In order to study the quantitative evaluation of the effectiveness of intelligent vehicles from the user’s working condition to the test condition in proving ground,based on the theory of driving risk field,considering 4 evaluation factors:acceleration coefficient,risk degree coverage rate,maximum risk degree and risk degree distribution,the associated matching model and the evaluation model of intelligent vehicle test condition and user condition are proposed.Based on the concept of"equal risk",the internal relationship between test condition and user’s condition is established in the associated matching model,and the principle that the distributions of risk and working cycles should be consistent as much as possible is put forward.Based on the"effectiveness indicator",the quantitative evaluation indicator of test condition is established.Four principles are put forward:the larger the acceleration coefficient the better,the larger the sample risk degree coverage rate the better,the closer the maximum risk degree is to the user limit the better,and the higher the similarity of risk degree distribution the better.The case study of the vehicle following scenario is performed to verify the correlation evaluation model,indicating that the risk degrees of the 3 test conditions are close to the user’s risk degree,which meets the requirements of the matching model.The effectiveness indicators of the 3 test conditions are calculated and sequenced according to the values.When the weight distribution of the 4 evaluation factors is changed,the sequence changes.The research result shows that the correlation evaluation model can be used for quantitative evaluation of the effectiveness of intelligent vehicle test conditions,and the allocation of evaluation factor weight will affect the effectiveness indicator,which can be set on demand.