Google to drive optimised Fords
Software that can record and eventually predict driver behaviours and routines is being tested by Ford to deliver improved fuel economyUsing the Google Prediction Application Programming Interface (API) Ford is hoping to improve vehicle performance and efficiency by optimising systems based on predicted driver behaviour.
The idea behind Google's new Prediction API is to make smart applications even smarter. Basically applications can use algorithms to analyse historic data and then predict the most likely future actions.
Ford believes there is potential here to utilise the Prediction API algorithms to optimise vehicle configurations on the fly based on the driving conditions, destination, driver personality and more.
"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."
This week, Ford researchers are presenting a conceptual case of how the Google Prediction API could alter the performance of a plug-in hybrid electric vehicle at the 2011 Google I/O developer conference. In this theoretical situation, here's how the technology could work:
- After a vehicle owner opts in to use the service, an encrypted driver data usage profile is built based on routes and time of travel. In essence, the system learns key information about how the driver is using the vehicle
- Upon starting the vehicle, Google Prediction will use historical driving behaviour to evaluate (given the current time of day and location to develop a prediction) the most likely destination and how to optimise driving performance to and from that location
- An on-board computer might say, "Good morning, are you going to work?" If the driver is in fact going to work, the response would be, "Yes," and then an optimised powertrain control strategy would be created for the trip. A predicted route of travel could include an area restricted to electric-only driving. Therefore, the plug-in hybrid could program itself to optimise energy usage over the total distance of the route in order to preserve enough battery power to switch to all-electric mode when traveling within 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 optimise its performance, even against factors that the driver may not be aware of, such as an EV-only zone," said McGee.
Because of the large amount of computing power necessary to make the predictions and optimisations, an off-board system is currently necessary. In essence, the car would transmit driving data to a data centre where the numbers can be crunched by powerful server technology.
The plan is that by understanding driver behaviours and patterns and correlating this to overall fuel and energy efficiency, templates for driving scenarios linked to vehicle configurations will emerge.
"Anticipating the driver's destination is just one way that Ford is investigating predicting driver behaviours," said McGee. "This information can ultimately be used to optimise vehicle performance attributes such as fuel efficiency and driveability."
The Google Prediction API is one example of a technology that is helping Ford open doors to new predictive possibilities powered by the cloud (cloud is a multi tenant infrastructure system owned by Google).
"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."
Other future considerations could help drivers comply with proposed regulations or geographic needs. For example, the French government is considering creating zones that would mandate vehicles have lower emissions. Cities such as London, Berlin, and Stockholm already have such zones.
If a vehicle were able to predict exactly when it might be entering such a zone, it could optimise itself in a way to comply with regulations, such as switching the engine to all-electric mode.
Integral to this next-step work is personal information security, an issue that is of the utmost importance to Ford. "We realise 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," notes 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.
"It's about pure customer benefit and creating individualised and optimised experiences – the right one for each person, vehicle and situation."
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