Liu, X., Apriaskar, E. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2024) Deep reinforcement learning method for control of mixed autonomy traffic systems. In: Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), 04-06 Sep 2024, Pilsen, Czech Republic. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9798350368048
Abstract
The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | autonomous vehicles; deep reinforcement; learning; proximal policy optimization; SUMO |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Sep 2024 16:11 |
Last Modified: | 21 Oct 2024 14:37 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/MFI62651.2024.10705775 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:217480 |