Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles
at Complex and Unsignalized Intersections
- URL: http://arxiv.org/abs/2301.05294v2
- Date: Fri, 20 Oct 2023 00:12:49 GMT
- Title: Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles
at Complex and Unsignalized Intersections
- Authors: Dawei Wang, Weizi Li, Lei Zhu, Jia Pan
- Abstract summary: We propose a decentralized multi-agent reinforcement learning approach for the control and coordination of mixed traffic at real-world, complex intersections.
In particular, we show that using 5% RVs, we can prevent congestion formation inside a complex intersection under the actual traffic demand of 700 vehicles per hour.
Our method is also robust against both blackout events and sudden RV percentage drops, and enjoys excellent generalizablility.
- Score: 36.059560636577146
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Intersections are essential road infrastructures for traffic in modern
metropolises. However, they can also be the bottleneck of traffic flows as a
result of traffic incidents or the absence of traffic coordination mechanisms
such as traffic lights. Recently, various control and coordination mechanisms
that are beyond traditional control methods have been proposed to improve the
efficiency of intersection traffic. Amongst these methods, the control of
foreseeable mixed traffic that consists of human-driven vehicles (HVs) and
robot vehicles (RVs) has emerged. In this project, we propose a decentralized
multi-agent reinforcement learning approach for the control and coordination of
mixed traffic at real-world, complex intersections--a topic that has not been
previously explored. Comprehensive experiments are conducted to show the
effectiveness of our approach. In particular, we show that using 5% RVs, we can
prevent congestion formation inside a complex intersection under the actual
traffic demand of 700 vehicles per hour. In contrast, without RVs, congestion
starts to develop when the traffic demand reaches as low as 200 vehicles per
hour. When there exist more than 60% RVs in traffic, our method starts to
achieve comparable or even better performance to traffic signals on the average
waiting time of all vehicles at the intersection. Our method is also robust
against both blackout events and sudden RV percentage drops, and enjoys
excellent generalizablility, which is illustrated by its successful deployment
in two unseen intersections.
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