The invention relates to the field of unmanned driving, in particular to an ID3 decision-making tree-based unmanned driving decision-making method, and solves the problem that a decision-making methodin the prior art is not real-time and accurate enough. The method comprises the following steps: A, calculating the information entropy required by classification of a given sample; B, calculating the information gain of each feature; c, selecting a root node or an internal node; and D, a division end mark: stopping division when only one category label exists in the subset. The invention discloses an unmanned driving decision-making method based on an ID3 decision-making tree. According to the technology, a decision tree algorithm is selected; on the basis of the BrechtelS method, the driving behavior decision model based on the ID3 decision tree is constructed by utilizing the advantages of the decision tree in the aspects of knowledge automatic acquisition and accurate expression, andreal-time and accurate identification of the current driving behavior and rapid decision of the behavior at the next moment are realized.
ID3 decision-making tree-based unmanned driving decision-making method