CPC G06N 3/082 (2013.01) [G06N 3/0464 (2023.01); G06V 30/274 (2022.01)] | 20 Claims |
1. A method for constructing an explainable user output for control of a vehicle, the method comprising:
receiving input data for the vehicle of a set of features for processing at a neural network (NN) that is stored within a non-transitory computer readable storage medium of the vehicle, wherein the neural network is composed of a plurality of layers of an original classifier, wherein the original classifier has been frozen with a set of weights;
determining, via a processor of the vehicle, a semantic function to categorize a data sample with a semantic category;
determining, via the processor of the vehicle, a level of semantic accuracy for each layer of the plurality of layers of the original classifier within the neural network wherein the original classifier is a trained model;
computing, via the processor of the vehicle, a representative vector with average activations of a layer's nodes of the plurality of layers for training samples of a semantic category determined for evaluation by computing distances of samples in a test set from available layers for each semantic category;
computing, via the processor of the vehicle, a number of test samples for each layer and for each semantic category of a plurality of semantic categories, that are closest to each other in each layer, to designate a layer with a highest score representative of a best semantics in each test sample of a set of test sample processed;
extending, via the processor of the vehicle, a designated layer of the plurality of layers by a category branch to the NN to extract semantic data samples of the semantic content, and for an explainable classifier in the extension of the NN for defining a plurality of semantic categories determined by the NN;
training, via the processor of the vehicle, a set of connections of the explainable classifier of the NN to compute a set of output explanations with an accuracy measure associated with each output explanation based on at least one semantic category of the plurality of semantic categories;
comparing the accuracy measure for each output explanation based on an extracted semantic data sample by the trained explainable classifier for each semantic category to generate an output explanation in a user understandable format;
automatically controlling movement of the vehicle, via the processor, based on the input data and the neural network; and
providing, via the processor, an output for a user of the vehicle that includes an explanation of the controlling of the movement of the vehicle, using the output explanation.
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