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Reinforcement Learning-based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Alms, Robert and Noulis, Aristeidis and Mintsis, Evangelos and Lücken, Leonhard and Wagner, Peter (2022) Reinforcement Learning-based Traffic Control: Mitigating the Adverse Impacts of Control Transitions. IEEE Open Journal of Intelligent Transportation Systems, pp. 187-198. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/OJITS.2022.3158688. ISSN 2687-7813.

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An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.

Item URL in elib:https://elib.dlr.de/143411/
Document Type:Article
Title:Reinforcement Learning-based Traffic Control: Mitigating the Adverse Impacts of Control Transitions
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Alms, RobertRobert.Alms (at) dlr.dehttps://orcid.org/0000-0001-9950-3596
Noulis, AristeidisDLRhttps://orcid.org/0000-0003-1822-2050
Mintsis, EvangelosCERTH-HIThttps://orcid.org/0000-0002-2599-8642
Lücken, LeonhardLeonhard.Luecken (at) uol.dehttps://orcid.org/0000-0001-6103-6531
Wagner, Peterpeter.wagner (at) dlr.dehttps://orcid.org/0000-0001-9097-8026
Date:11 March 2022
Journal or Publication Title:IEEE Open Journal of Intelligent Transportation Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.1109/OJITS.2022.3158688
Page Range:pp. 187-198
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:IEEE
Keywords:Connected automated vehicles (CAV), reinforcement learning (RL), take-over request (ToR), traffic management (TM), transition of control (ToC).
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems
Institute of Transportation Systems > Cooperative Systems, BA
Deposited By: Alms, Robert
Deposited On:25 Mar 2022 13:24
Last Modified:25 Mar 2022 13:24

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