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Audi Increases Fuel Economy by Monitoring Traffic Lights

Audi Traffic Light Recognition System Could Improve Fuel Economy by 15%
Audi Traffic Light Recognition System Could Improve Fuel Economy by 15%

How you drive has just as much impact on your fuel economy as what you drive, and Audi Traffic Light Recognition could help drivers save even more.

Today’s automobiles, whether conventional or electrified, are delivering better fuel economy every year. In fact, the average fuel economy in the United States hit an all-time high fuel economy rating of 24.9 mpg (miles per gallon). Unfortunately, the same researchers that calculated that number, at the University of Michigan Traffic Research Institute (UMTRI), found that driver habit pretty much cancels out these improvements.

The vehicles themselves are getting better fuel economy, but drivers aren’t learning to drive more economically. Some vehicles teach their green-thinking drivers to drive better, such as avoiding heavy acceleration and excessive speed. The Toyota Prius, for example, has a power meter mode on the multi-information display, which displays how much power you are using. Outside of the middle zone, giving the car too much accelerator, pushes into the red, reminding the driver to back off a little, to improve fuel economy.

The Audi Traffic Light Recognition (TLR) system, according to their trials in Las Vegas, Nevada, could boost fuel economy another 15%. Audi TLR communicates with traffic lights, noting how long a light will stay red, for example, and times its engine SST (start-stop technology) to restart the engine five seconds before the light will turn green again. The five-second timer is to alert the driver the light’s about the turn green.

Another part of Audi TLR’s programming will let the driver know what speed he should drive to get to the next light by the time it turns green. This improves fuel economy by helping the driver to avoid unnecessary speeding, jackrabbit starts, and unnecessary stops. Audi says this technology, if approved by regulators, could be integrated into any of their models.

Photo credit: Horia Varlan Foter CC BY

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