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Scalable Multiagent Driving Policies For Reducing Traffic Congestion.
Jiaxun
Cui, William Macke, Harel
Yedidsion, Aastha Goyal, Daniel Urieli, and Peter
Stone.
In Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
May 2021.
Project page, with videos
Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reflects open network traffic efficiency. Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles). Additionally, our modular transfer learning approach saves up to 80% of the training time in our experiments, by focusing its data collection on key locations in the network. Finally, we show for the first time a distributed multiagent policy that improves congestion over human-driven traffic. The distributed approach is more realistic and practical, as it relies solely on existing sensing and actuation capabilities, and does not require adding new communication infrastructure.
@InProceedings{AAMAS21-cui,
author = {Jiaxun Cui and William Macke and Harel Yedidsion and Aastha Goyal and Daniel Urieli and Peter Stone},
title = {Scalable Multiagent Driving Policies For Reducing Traffic Congestion},
booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
location = {Virtual},
month = {May},
year = {2021},
abstract = {
Traffic congestion is a major challenge in modern urban
settings. The industry-wide development of autonomous
and automated vehicles (AVs) motivates the question of
how can AVs contribute to congestion reduction. Past
research has shown that in small scale mixed traffic
scenarios with both AVs and human-driven vehicles, a
small fraction of AVs executing a controlled multiagent
driving policy can mitigate congestion. In this paper,
we scale up existing approaches and develop new
multiagent driving policies for AVs in scenarios with
greater complexity. We start by showing that a
congestion metric used by past research is manipulable
in open road network scenarios where vehicles
dynamically join and leave the road. We then propose
using a different metric that is robust to manipulation
and reflects open network traffic efficiency. Next, we
propose a modular transfer reinforcement learning
approach, and use it to scale up a multiagent driving
policy to outperform human-like traffic and existing
approaches in a simulated realistic scenario, which is
an order of magnitude larger than past scenarios
(hundreds instead of tens of vehicles). Additionally,
our modular transfer learning approach saves up to 80%
of the training time in our experiments, by focusing its
data collection on key locations in the
network. Finally, we show for the first time a
distributed multiagent policy that improves congestion
over human-driven traffic. The distributed approach is
more realistic and practical, as it relies solely on
existing sensing and actuation capabilities, and does
not require adding new communication infrastructure.
},
wwwnote={<a href="https://www.cs.utexas.edu/~aim/flow.html" target="_blank">Project page, with videos</a>},
}
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