Ford Global Technolgoies, LLC

Ghassan AlRegib

Mohit Prabhushankar

David McCreadie

Mostafa Parchami

Daniel Lewis Boston

Enrique Corona

Renuka Vikram Gokhale

Donna L Bell

Dogancan Temel

Michael Robert Tinskey

Armin Parchami

Gukyeong Kwon

Tariq Alshawi

Mohammed Aabed

Berdinus Bras

Min-Hung Chen

Ryan Benkert

Neural networks and learning algorithms can use a variance of gradients to provide a heuristic understanding of the model. The variance of gradients can be used in active learning techniques to train a neural network. Techniques include receiving a dataset with a vector. The dataset can be annotated and a loss calculated. The loss value can be used to update the neural network through backpropagation. An updated dataset can be used to calculate additional losses. The loss values can be added to a pool of gradients. A variance of gradients can be calculated from the pool of gradient vectors. The variance of gradients can be used to update a neural network.

Variance of gradient based active learning framework for training perception algorithms

A deep neural network (DNN) can be trained based on a first training dataset that includes first images including annotated first objects. The DNN can be tested based on the first training dataset to determine first object predictions including first uncertainties. The DNN can be tested by inputting a second training dataset and outputting first object predictions including second uncertainties, wherein the second training dataset includes second images including unannotated second objects. A subset of images included in the second training dataset can be selected based on the second uncertainties, The second objects in the selected subset of images included in the second training dataset can be annotated. The DNN can be trained based on the selected subset of images included in the second training dataset including the annotated second objects.

Neural network training

A computing device, programmed to: acquire a color image and transform the color image into a color-component map. The computer can be further programmed to process the color-component map to detect a traffic sign by determining spatial coincidence and determining temporal consistency between the color-component map and the traffic sign.

Color learning

A plurality of data streams are received from a vehicle, at least one of the data streams including multimedia data. A prioritization of the data streams is performed according to one or more factors. At least one of adjusting at least one of the data streams and preventing transmission of at least one of the data streams according to the prioritization is performed.

Remote vehicle control and operation

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Johns Creek, GA, United States of America

Ghassan AlRegib