AI Case Study
Berkeley Lab releases reinforcement learning training platform for training autonomous vehicles on traffic regulation
The US Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has developed a driving simulation platform using reinforcement learning, with the goal of using it to train autonomous vehicles how to regulate traffic congestion.
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Automobiles And Parts
"The traffic-smoothing project, dubbed CIRCLES, or Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing, is led by Berkeley Lab researcher Alexandre Bayen... 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.
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."
R And D
"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".
At time of writing, benchmarks were to be released within the month.
"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... 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."