Ford Global Technolgoies, LLC

Ryan Lee Baker

Timothy Mark Feldkamp

Jianbo Lu

Fling Finn Tseng

Joseph F Stanek

John V James

Dimitar Petrov Filev

John Ottavio Michelini

Hongtei Eric Tseng

Pankaj Kumar

Johannes Geir Kristinsson

Anthony Mario D'Amato

Margherita Zanini-Fisher

Medville Jay Throop

Shankar Narayan Mohan

Arun Dutta

David Scott Rohweder

Mahmoud A Abou-Nasr

Arpan Kusari

Subramanya Nageshrao

Christopher Cruise

Dennis Carl Krzyske

Leda Daehler

Kelly Trost

Daniel Haakenson

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

A method for identifying vehicle vibration includes receiving signals indicative of a vehicle vibration from one or more vehicle sensors while the vehicle is in operation. The method also includes determining a magnitude of the vehicle vibration at one or more target frequency bands. The method additionally includes determining whether the vehicle vibration is associated with one or more wheels of the vehicle based on the magnitude of the vehicle vibration at the one or more target frequency bands. The method further includes generating a diagnostic recommendation when the vehicle vibration is associated with the one or more wheels of the vehicle.

Arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle

A vehicle system includes a driver identification fusion module programmed to receive a confidence level from a plurality of identification modules. Each confidence level indicates the likelihood that a driver of a vehicle is a particular person. The driver identification fusion module is programmed to aggregate the confidences levels received from the plurality of identification modules and output an aggregated confidence level signal representing an identity of the driver based at least in part on the confidence levels received from the plurality of identification modules.

Adaptive driver identification fusion

A system includes a processor configured to gather historical risk-affecting data with respect to a current road. The processor is also configured to gather current risk-affecting data with respect to the current road. Further, the processor is configured to generate a baseline risk index for the road based on the historical data. The processor is additionally configured to modify the baseline risk index based on the current data and provide a risk index value for the current road based on the modified baseline risk index

Method and apparatus for road risk indices generation

A method for estimating a cylinder specific performance parameter for a specific cylinder of an internal combustion engine for each firing attempt, which relies on production sensors commonly equipped onboard production vehicles, is disclosed.

Cylinder specific performance parameter computed for an internal combustion engine

An engine cylinder firing event is intentionally defeated by clipping the flyback voltage of the ignition system primary coil winding under the control of a misfire generator that develops a misfire logic signal from ignition dwell events and user inputs and also develops a signal indicative of which cylinder firing event was defeated.

Method and apparatus for generating and identifying misfires

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Dearborn Heights, MI, United States of America

Ryan Lee Baker