The invention belongs to the technical field of driving style identification, and particularly relates to a driver driving style identification method based on an SHAP interpretation method and a random forest. Comprising the following steps: 1, selecting a dimensional basic driving signal based on collected original driving data, and carrying out dimensionless processing on the dimensional basic driving signal in a time domain and a frequency domain to obtain a dimensionless input characteristic parameter; 2, performing dimensionality reduction processing on the dimensionless input characteristic parameters by adopting an SHAP interpretation method, and screening out the dimensionless input characteristic parameters which are effective for identifying the driving style of the driver from all the dimensionless input characteristic parameters; and 3, designing a driving style identification model based on a random forest machine learning algorithm, inputting the dimensionless input characteristic parameters obtained after screening into the driving style identification model based on the random forest machine learning algorithm, and accurately identifying the driving style of the driver. The complexity of a driving style identification model can be reduced, and the driver driving style identification precision is improved.
本发明属于驾驶风格辨识技术领域,具体的说是一种基于SHAP解释法与随机森林的驾驶人驾驶风格辨识方法。包括:一:基于采集得到的原始驾驶数据,挑选有量纲基础驾驶信号,并对有量纲基础驾驶信号在时域与频域上,进行无量纲化处理,获得无量纲输入特征参数;二:采用SHAP解释法对无量纲输入特征参数进行降维处理,从全部无量纲输入特征参数中,筛选出对驾驶员驾驶风格辨识有效的无量纲输入特征参数。三:设计基于随机森林机器学习算法的驾驶风格辨识模型,将筛选后获得的无量纲输入特征参数输入至基于随机森林机器学习算法的驾驶风格辨识模型后,精确辨识驾驶员驾驶风格。本发明能够降低驾驶风格辨识模型复杂度,提高驾驶员驾驶风格辨识精度。
Driver driving style identification method based on SHAP interpretation method and random forest
一种基于SHAP解释法与随机森林的驾驶人驾驶风格辨识方法
21.03.2023
Patent
Elektronische Ressource
Chinesisch
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