Ford Global Technolgoies, LLC

Christopher Cruise

Dimitar Petrov Filev

Hongtei Eric Tseng

Shankar Narayan Mohan

Ryan Lee Baker

Akhil Perincherry

Arpan Kusari

Leda Daehler

Subramanya Nageshrao

A system and a method can receive a first dataset having a first label and a first context. The system and the method can also generate, at the trained deep neural network, a second dataset having the first label and a second context according to a mapping, wherein a first mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having the first label and the first context to data having a second label and the first context and a second mapping of the plurality of mapping comprises one or more weights of the trained deep neural network that maps data having a second label and the first context to data having the second label and the second context, wherein the second context is different from the first context and the second label is different from the first label.

Domain generation via learned partial domain translations

A computing system can determine a vehicle action based on inputting vehicle sensor data to a first neural network including a first safety agent that can determine a probability of unsafe vehicle operation. The first neural network can be adapted, at a plurality of times, by a periodically retrained deep reinforcement learning agent that includes a second deep neural network including a second safety agent. A vehicle can be operated based on the vehicle action.

Vehicle adaptive learning

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Farmington Hills, MI, United States of America

Christopher Cruise