SAN FRANCISCO – Ford is using Google’s Prediction API software technology to identify techniques that could help the automaker build vehicles capable of independently altering their performance to improve fuel efficiency and driveability.
Ford said its researchers are applying Google’s Prediction API to two years of the automaker’s own predictive driver behavior research and analysis. The Google API can convert information, such as historical driving data, for example locations a driver traveled to and the time of day for the trip, into real-time predictions of where a driver is traveling to at a given departure time. The automaker is hoping to use this data, which would be stored in the “cloud,” to develop automotive systems that would allow a vehicle to optimize its performance based on driving route, for example.
“The Google Prediction API allows us to utilize information that an individual driver creates over time and make that information actionable,” said Ryan McGee, technical expert, Vehicle Controls Architecture and Algorithm Design, Ford Research and Innovation. “Between Google Prediction and our own research, we are discovering ways to make information work for the driver and help deliver optimal vehicle performance.”
Ford gave an example where, after a vehicle owner opts into the service (in the case of fleets, a company decides to opt into using this service) the system would build an encrypted driver data usage profile based on routes and travel times. After starting the vehicle, the system would use this prediction technology to use historical driving behavior, along with current location and time of day, to predict the most likely destination and optimize driving performance to and from that destination.
The system would allow a driver to change destinations if needed. Ford gave the example of an on-board computer checking whether the driver is traveling to work, and if the driver says yes, the vehicle would optimize the powertrain control strategy for the trip to work.
In the case of a plug-in hybrid, the predicted route could include an area restricted to electric-only driving, and the vehicle could program itself to optimize energy use during the trip to preserve enough battery power to switch to all-electric mode in the EV-only zone.
“Once the destination is confirmed, the vehicle would have instant access to a variety of real-time information so it can optimize its performance, even against factors that the driver may not be aware of, such as an EV-only zone,” said McGee.
The Google Prediction API is one example of a technology that is helping Ford open doors to new predictive possibilities powered by the cloud.
“Ford already offers cloud-based services through Ford SYNC®, but those services thus far have been used for infotainment, navigation and real-time traffic purposes to empower the driver,” said Johannes Kristinsson, system architect, Vehicle Controls Architecture and Algorithm Design, Ford Research and Innovation. “This technology has the potential to empower our vehicles to anticipate the driver's needs.”
The automaker said it is now studying the feasibility of incorporating other driver-specific behaviors, such as driver style and habits, into the optimization process, which Ford said would allow the vehicle and driver to work together to maximize energy efficiency. Ford said that information security is a critical part of this research.
“We realize that the nature of this research includes the use of personal data and location awareness, something we are committed to protecting for our customers in everything we do,” said Kristinsson. “A key component of this project is looking at how to develop secure personal profiles that will ensure appropriate levels of protection and specific data use only by the driver and the vehicle to deliver the best driving experience.”
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