Many traffic jams are caused by human behavior: a slight tap on the brakes can ripple through a line of cars, triggering a slowdown - or complete gridlock - for no apparent reason.
But in a massive traffic experiment that occurred outside of Nashville last week, scientists tested whether introducing just a few AI-equipped vehicles to the road can help ease these "phantom" jams and reduce fuel consumption for everyone. The answer seems to be yes.
Over the course of five days, researchers conducted one of the largest traffic experiments of its kind in the world, deploying a fleet of 100 Nissan Rogue, Toyota RAV4 and Cadillac XT5 vehicles onto a busy stretch of Nashville's I-24 during the morning commute. Each vehicle was equipped with an AI-powered cruise control system designed to automatically adjust the speed of the vehicle to improve the overall flow of traffic - essentially turning each car into its own "robot traffic manager."
"Driving is very intuitive. If there's a gap in front of you, you accelerate. If someone brakes, you slow down. But it turns out that this very normal reaction can lead to stop-and-go traffic and energy inefficiency. That's precisely what AI technology is able to fix - it can direct the vehicle to things that are not intuitive to humans, but are overall more efficient."
The Key Finding: 40% Fuel Savings
In 2016, a team of researchers including Work and Delle Monache conducted a real-world experiment showing the profound impact smart vehicles could have on the flow of traffic.
In the experiment, 20 cars were driven on a closed, circular track. When all the cars were driven by humans, traffic "waves" consistently emerged, mimicking the stop-and-go pattern that occurs on roadways.
But adding just one smart vehicle to the mix smoothed the human-caused waves, leading to a 40% fuel savings overall.
Benefits for Everyone
"By conducting the experiment at this large of a scale, we hope to show that our results can be reproduced at the societal level," said CIRCLES co-PI Maria Laura Delle Monache, an assistant professor of civil and environmental engineering at UC Berkeley. "Even when only a few vehicles behave differently, the overall system can be impacted, making it better for everyone on the road and not only for those with AI-equipped vehicles."
"Stop-and-go traffic creates a lot of problems. Constantly starting and stopping wastes a lot of energy. It's also uncomfortable for drivers and passengers, and can increase the likelihood of collisions. By smoothing out that flow, we hope to make driving not only safer and more energy efficient, but more comfortable as well."
From Traffic Monitoring to Traffic Smoothing
For more than a decade, Bayen and other members of the CIRCLES consortium have been applying the latest technologies to help improve transportation. In 2008, Bayen and Daniel Work, who was a UC Berkeley graduate student at the time, led the Mobile Millennium project, one of the first demonstrations of how GPS-enabled smartphones can provide real-time information about traffic conditions.
Now that smartphones are ubiquitous and real-time traffic information is available at the click of a button, Bayen is excited to show how machine learning can be used to not only monitor traffic but also improve conditions on the road.
"The beauty of the techniques we're using is that they can take human data, learn from it, and then apply it to make things better."
Creating "Socially Acceptable" AI
As part of the CIRCLES consortium, UC Berkeley researchers have taken the lead in developing the machine learning algorithms that govern how fast AI-powered vehicles should go. These algorithms, also called "speed planners" and "controllers," use information about overall traffic conditions and the vehicle's immediate surroundings to determine the best speed for improving traffic flow.
"The idea is that, if a traffic jam or bottleneck appears ahead on the road, we want to try to adjust the speed of the vehicle so that it doesn't contribute to the congestion," said Hossein Nick Zinat Matin, a postdoctoral researcher at UC Berkeley. "This is a complex mathematical problem."
Testing the software in the field is also important to ensure that the AI-powered vehicles don't behave in ways that might be considered "socially unacceptable" to humans. For instance, vehicles may smooth traffic by maintaining a slow, steady speed, rather than constantly accelerating and braking. However, slow driving may open large gaps in traffic, which could anger other drivers, or allow other cars to cut in.
"We want to train our vehicles to drive in a specific way that is not human-like, but also not completely socially unacceptable. A big focus for us during the test week was to make daily tweaks to our controllers based on feedback from our drivers."
The Future
"Our vision is that eventually, this technology will be deployed in many, if not all, vehicles, and we are working on ways to make it scalable to the public," Lee said.
The game changer here was the coordination - the fact that the vehicles leverage each other's presence and can react preemptively to downstream traffic conditions.
Research Support
CIRCLES Consortium research is supported by the National Science Foundation, the U.S. Department of Transportation and the U.S. Department of Energy. Additional funding was provided by Nissan, Toyota North America, General Motors, the Federal Highway Administration, the Tennessee Department of Transportation, the California Department of Transportation, and many other partners.