Researchers at the
Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab)
have proven that applying AI to self-driving cars to smooth traffic, reduce
fuel consumption, and improve air quality predictions is no longer the stuff of
science fiction by launching two research projects to do just that.
In collaboration with UC
Berkeley, Berkeley Lab scientists are using deep reinforcement learning, a
computational tool for training controllers, to make transportation more
sustainable. One project uses deep reinforcement learning to train autonomous
vehicles to drive in ways to simultaneously improve traffic flow and reduce
energy consumption. A second uses deep learning algorithms to analyze satellite
images combined with traffic information from cell phones and data already
being collected by environmental sensors to improve air quality predictions.
“Thirty percent of energy
use in the U.S. is to transport people and goods, and this energy consumption
contributes to air pollution, including approximately half of all nitrogen
oxide emissions, a precursor to particular matter and ozone – and black carbon
(soot) emissions,” said Tom Kirchstetter, director of Berkeley Lab’s Energy
Analysis and Environmental Impacts Division, an adjunct professor at UC
Berkeley, and a member of the research team.
“Applying machine learning technologies to transportation and the environment is a new frontier
that could pay significant dividends – for energy as well as for human health.”
Traffic smoothing with Flow
CIRCLES, or Congestion
Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing, is led by
Berkeley Lab researcher Alexandre Bayen, who is also is a professor of electrical
engineering and computer science at UC Berkeley and director of UC Berkeley’s
Institute of Transportation Studies. CIRCLES is based on a software framework
called Flow, developed by Bayen’s team of students and post-doctoral
researchers.
Flow is a
first-of-its-kind software framework allowing researchers to discover and
benchmark schemes for optimizing traffic. Using a state-of-the-art open-source
microsimulator, Flow can simulate hundreds of thousands of vehicles – some
driven by humans, others autonomous – driving in custom traffic scenarios.
“The potential for cities
is enormous,” said Bayen. “Experiments have shown that the energy savings with
just a small percentage of vehicles on the road being autonomous can be huge.
And we can improve it even further with our algorithms.”
Flow was launched in 2017
and released to the public in September, and the benchmarks are being released
this month. With funding from the Laboratory Directed Research and Development
program, Bayen and his team will use Flow to design, test, and deploy the first
connected and autonomous vehicle (CAV)-enabled system to actively reduce
stop-and-go phantom traffic jams on freeways.
Reducing congestion by reinforcement learning
A simple experiment done
by Japanese researchers 10 years ago motivated some of the today researchers to
use autonomous vehicles in smoothing traffic. Even though every is proceeding
smoothly at first, the traffic waves start and cars come to a standstill 30
seconds later.
“You have stop-and-go oscillation
within less than a minute,” Bayen said. “This experiment led to hundreds if not
thousands of research papers to try to explain what is happening.”
There was one change of
the same experiment, created by the team of researchers of Vanderbilt University,
led by Dan Work: a single autonomous vehicle was added in the ring. The
oscillations are immediately smoothed out as soon as the automation is turned
on.
Why? “The automation
essentially understands to not accelerate and catch up with the previous person
– which would amplify the instability – but rather to behave as a flow
pacifier, essentially smoothing down by restraining traffic so that it doesn’t
amplify the instability,” Bayen said.
Deep reinforcement
learning has been used to train computers to play chess and to teach a robot
how to run an obstacle course. It trains by “taking observations of the system,
and then iteratively trying out a bunch of actions, seeing if they’re good or
bad, and then picking out which actions it should prioritize,” said Eugene
Vinitsky, a graduate student working with Bayen and one of Flow’s developers.
In the case of traffic,
Flow trains vehicles to check what the cars directly in front of and behind
them are doing. “It tries out different things – it can accelerate, decelerate,
or change lanes, for example,” Vinitsky explained. “You give it a reward
signal, like, was traffic stopped or flowing smoothly, and it tries to correlate
what it was doing to the state of the traffic.”
With the CIRCLES project,
Bayen and his team plan to first run simulations to confirm that significant
energy savings result from using the algorithms in autonomous vehicles. Next
they will run a field test of the algorithm with human drivers responding to
real-time commands.
DeepAir
{Marta Gonzalez, a
professor in UC Berkeley’s City & Regional Planning Department, has
established a pollution project called DeepAir (Deep Learning and Satellite Imaginary
to Estimate Air Quality Impact at Scale). In one of her researches, Marta has
recommended people to use electric vehicle charging schemes to save energy and
cost after using cell phone data to study how people move around cities.}
For this project, she
will take advantage of the power of deep learning algorithms to analyze
satellite images combined with traffic information from cell phones and data
already being collected by environmental monitoring stations.
“The novelty here is that
while the environmental models, which show the interaction of pollutants with
weather – such as wind speed, pressure, precipitation, and temperature – have
been developed for years, there’s a missing piece,” Gonzalez said. “In order to
be reliable, those models need to have good inventories of what’s entering the
environment, such as emissions from vehicles and power plants.
“We bring novel data
sources such as mobile phones, integrated with satellite images. In order to
process and interpret all this information, we use machine learning models
applied to computer vision. The integration of information technologies to
better understand complex natural system interactions at large scale is the
innovative piece of DeepAir.”
{Resulting analysis is
predicted by the researchers to allow them to gain deep information about the
sources and distributions of pollutants, so they could make a more efficient
and timely interventions. Take this as an example, In the “Spare the Air” days
of the Bay Aera, the traffic restrictions are voluntary, and other cities form
plans to restrict traffic or industry.}
While the idea of using
algorithms to control cars and traffic may sound incredible at the moment,
Bayen believes technology is headed in that direction. “I do believe that
within 10 years the things we’re coming up with here, like flow smoothing, will
be standard practice, because there will be more automated vehicles on the
road,” he said.
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