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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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Abstract

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
Authors:
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 SCOPUS:No
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
ISSN:2687-7813
Status:Published
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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