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Researchers at Ford are testing a hybrid gas-electric car that makes an educated guess at where you’re going whenever you turn the key. 

They installed software that draws on prediction technology developed by Google in a plug-in hybrid Ford Escape SUV. To make the car more energy-efficient that software directs the car’s computer to tweak how its electric motor draws power from the vehicle’s battery and gas generator during a drive according to the trip a driver is expected to make.

“The system keeps track of how a person uses their car and builds a predictive model in the cloud, using Google’s prediction technology,” says Ryan McGee, of Ford’s Dearborn, Michigan, research labs. “When you start the car, it asks that model, ‘Where are we going next?’”

Ford’s prototype makes use of a Google service called the Prediction API to create, store, and query that model. When data is uploaded to the service, machine-learning algorithms build a model that can be used to predict future additions to the data set.

In the case of the Ford prototype, the car uses a wireless Internet connection to supply the prediction service with the vehicle’s current location and the time. It receives back a ranked list of likely trips. Based on that list, the software can inform the car to change the way its engine management software juggles gas and electric power consumption over the trip. “It might use electric energy earlier in the trip, or save it for the end,” based on rules set by the driver, or derived by the car’s software from past experience, says McGee.

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Credit: Ford

Tagged: Computing, Google, artificial intelligence, machine learning, cars, Ford, Predictive Modeling, predictive software

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