Ford Global Technolgoies, LLC

Colleen M Cauvet

Donald Paul Bilger

Arun Dutta

Nithya Somanath

Collin P Hurley

Nicholas Colella

Andrea Bowes Chowanic

David Alan Hamilton

Dave A Herman

Javier Onate

Upon determining a mobile object is approaching an ice layer above a body of water, a thermal image of the ice layer is obtained. The thermal image and ambient temperature data are input to a neural network that outputs a plurality of regions of the ice layer and respective estimated thicknesses for the regions. A classification for each region is determined based on its estimated thickness and the mobile object. The classification is one of preferred or nonpreferred. The classifications for the regions are output.

Ice thickness estimation for mobile object operation

In order to display driving assistance images to a driver in a vehicle, visible light images are captured using a visible light camera mounted for viewing a roadway on which the vehicle travels and infrared images overlapping with the visible light images are captured using an infrared camera mounted for viewing the roadway. The visible light images are normally displayed on a display screen when a visible light brightness around the vehicle is greater than a brightness threshold. Trigger conditions are monitored which are indicative of limitations of a driver visibility when directly viewing the roadway. The trigger conditions include a driver reaction. The monitored trigger condition is classified according to a plurality of predetermined classifications which indicate an occurrence of an impairment event. The infrared images are selected for display on the display screen upon occurrence of the impairment event.

Multi-camera imaging system selecting between visible and infrared views in a vehicle

Methods and apparatus to determine a predicted wait time at a commercial point-of-interest for an estimated time-of-arrival are disclosed. An example method includes determining an estimated time-of-arrival of a vehicle at a commercial point-of-interest and obtaining current wait-time data and historical wait-time data for the estimated time-of-arrival for the commercial point-of-interest. The example method includes determining, via a processor, a predicted wait time for the estimated time-of-arrival based on the current wait-time data and the historical wait-time data and communicating the predicted wait time to a user interface of the vehicle.

Predicting a point-of-interest wait time for an estimated time-of-arrival

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Dearborn, MI, United States of America

Colleen M Cauvet