The invention relates to an automatic driving vehicle highway driving suitability evaluation method based on ensemble learning, and the method comprises the following steps: collecting driving data and corresponding traffic data of an automatic driving vehicle driving on a highway, and carrying out the preprocessing and category extraction, and obtaining feature data; judging a lane detection failure type by using the lane width detection data; constructing a Boosting ensemble learning model, taking the feature data as input, taking the lane detection failure type as output, and training the model; and based on the trained Boosting ensemble learning model, predicting a lane detection failure type of the to-be-evaluated expressway, and obtaining an expressway driving suitability evaluation conclusion of the automatic driving vehicle. Compared with the prior art, the method achieves the construction of the nonlinear relation between the linear design of the expressway, the traffic marking attributes, the traffic operation and environment characteristics and the lane detection performance, and solves the problem that the expressway driving suitability of the automatic driving vehicle is difficult to quantitatively evaluate.
本发明涉及一种基于集成学习的自动驾驶车辆高速公路适驾性评估方法,包括以下步骤:采集自动驾驶车辆在高速公路行驶的驾驶数据和相应交通数据并进行预处理和类别提取,得到特征数据;利用车道宽度检测数据判断车道检测失效类型;构建Boosting集成学习模型,以特征数据作为输入,以车道检测失效类型作为输出,对模型进行训练;基于已训练的Boosting集成学习模型,预测待评估高速公路的车道检测失效类型,得出自动驾驶车辆高速公路适驾性评估结论。与现有技术相比,本发明实现了高速公路线形设计、交通标线属性、交通运行和环境特征与车道检测性能之间非线性关系的构建,解决了自动驾驶车辆高速公路适驾性难以量化评估的问题。
Method for evaluating expressway driving suitability of automatic driving vehicle based on ensemble learning
基于集成学习的自动驾驶车辆高速公路适驾性评估方法
2024-03-29
Patent
Elektronische Ressource
Chinesisch
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