Patent ID: 11934957
Assignee: GM GLOBAL TECHNOLOGY OPERATIONS LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system for constructing an explainable user output for control of a vehicle, the system comprising:
a processor of the vehicle, the processor configured to receive input data for the vehicle to process a set of features at a neural network 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 related to features of the input data;
the processor configured to determine a semantic function to categorize a data sample with a semantic category, and to determine 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;
the processor configured to compute a representative vector with average activations of each layer's nodes of the plurality of layers for training a set of samples of a semantic category to evaluate by computation of distances of each sample in a test set composed of a set of samples from available layers of the plurality of layers for each semantic category of a set of semantic categories;
the processor configured to compute a number of test samples for each layer of the plurality of layers and for each semantic category, that are closest to each other in each layer in the plurality of layers, to designate a layer of the plurality of layers with a highest score representative of a best semantics in each test samples wherein a designated layer is an optimal semantic content layer;
the processor configured to extend the optimal semantic content layer by a category branch to the neural network to extract a set of semantic data samples from the semantic content of the original classifier to the neural network, and to extend with an extension the explainable classifier of the neural network to define a plurality of semantic categories;
the processor configured to train a set of new connections of an explainable classifier of the neural network to compute a set of output explanations that are composed of explanations with an accuracy measure associated each output explanation based on at least one semantic category of the plurality of semantic categories;
the processor configured to compare the accuracy measure for each output explanation based on an extracted semantic data sample by the trained explainable classifier for each semantic category of the plurality of semantic categories and to generate the output explanation in a user understandable format;
the processor configured to automatically control movement of the vehicle based on the input data and the neural network; and
the processor configured to provide 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.