Ford Global Technolgoies, LLC

Leda Daehler

Dimitar Petrov Filev

Hongtei Eric Tseng

Jinesh J Jain

Gintaras Vincent Puskorius

Shankar Narayan Mohan

Gautham Sholingar

Ryan Lee Baker

Arpan Kusari

Subramanya Nageshrao

Christopher Cruise

A computing system can crop an image based on a width, height and location of a first vehicle in the image. The computing system can estimate a pose of the first vehicle based on inputting the cropped image and the width, height and location of the first vehicle into a deep neural network. The computing system can then operate a second vehicle based on the estimated pose. The computing system may train a model to identify a type and a location of a hazard according to the estimated pose, the hazard being such things as ice, mud, pothole, or other hazard. The model may be used by an autonomous vehicle to identify and avoid hazards or to provide drive assistance alerts.

Road surface characterization using pose observations of adjacent vehicles

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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Dearborn, MI, United States of America

Leda Daehler