No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.
No one likes sitting at a red light. But signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.
What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed.
In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.
Using simulations, they found that their approach reduces fuel consumption and emissions while improving average vehicle speed. The technique gets the best results if all cars on the road are autonomous, but even if only 25 percent use their control algorithm, it still leads to substantial fuel and emissions benefits.
Read more at: Massachusetts Institute of Technology
Photo Credit: xdigitalphotos via Pixabay
