Imagine a plug-in hybrid car that could learn your travel patterns. Now imagine it uses that info to keep you driving in electric-only mode more often.
Well, Ford is on it with its C-MAX Energi plug-in hybrid and Fusion Hybrid models.
The company calls its machine learning technology EV+, a patent-pending aspect of of SmartGauge®, which comes standard with these vehicles.
Ford research shows that hybrid owners prefer to drive in electric-only mode near their homes and frequent destinations.
To learn your often-visited locations, EV+ utilizes the onboard GPS of Ford SYNC® with proprietary software algorithms the company has developed in-house.
As EV+ gets familiar with your home parking spot and frequent destinations, it uses the electric power stored in the car’s high-voltage battery and remains in electric-only mode because you’re likely to charge it up again soon.
When EV+ identifies that your current location is within a radius of 1/8 mile or 200 meters of a frequent stop, the vehicle favors electric-only mode and the dashboard displays an “EV+” light to let you know.
Green Car Congress reports that Ford has big plans for big data and machine learning to transform the driver assistance capabilities of its vehicles. Among them are:
- “Big data” analysis for smarter decisions using radar and camera sensors to assess outdoor conditions like traffic congestion, so the vehicle could minimize distractions like an incoming phone call and keep the driver focused on the road.
- Biometrics for personalized driving help where biometric sensors in seats could measure the driver’s stress levels and integrated technologies calibrate what kind of assistance to provide based on the situation and the driver’s skill.
In our last post, we pointed to self-parking cars as an early manifestation of cognitive computing that enables humans to offload their duties to the vehicle entirely.
And in the previous post, we speculated how the Google hire of Ray Kurzweil could signal an acceleration of computers and humans extending one another’s capabilities.
With these trends in machine learning happening in tandem, how do you see driver assistance taking shape in the future?