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

Claim 17:
18. A vehicle comprising:
a propulsion system;
a non-transitory computer readable storage medium storing a neural network (NN);
and
at least one processor of a vehicle that is deployed in the vehicle, the at least one processor coupled to the propulsion system and to the non-transitory computer readable storage medium, the at least one processor programmed to:
receive input data for the vehicle of a set of a plurality of features for processing at the neural network (NN), wherein the neural network (NN) comprises one of a convoluted neural network (CNN) which 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;
determine a semantic function of a plurality of semantic functions to categorize a data sample with a semantic category;
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;
compute a representative vector with average activations of each layer's nodes of the plurality of layers for training samples of a semantic category for evaluation by computing distances of samples in a test set from available layers for each semantic category;
compute a number of test samples of a set of test samples for each layer and for each semantic category, that are closest to each other in each layer, to designate a layer of the plurality of layers with a highest score representative of a best semantics in each test samples;
redefine the semantic function to change the semantic accuracy to generate more semantic content in determining the semantic accuracy of each layer of the original classifier by the trained model wherein the redefinition of the semantic function is an optional step to provide more abstract semantic categories;
extend a designated layer by a category branch of the neural network to extract semantic data samples derived from the semantic content to the extension of the neural network, and to extend an explainable classifier of the neural network to define a plurality of semantic categories;
train a set of connections of the explainable classifier of the neural network to compute a set of output explanations with an accuracy measure associated each output explanation based on at least one semantic category of the plurality of semantic categories wherein the output explanations are well defined syntactic sentences;
compare 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 the output explanation in a user understandable format;
automatically control movement of the vehicle based on the input data and the neural network; and
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.