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Platooning Control with Deep Reinforcement Learning

Mirwald, Jonas (2019) Platooning Control with Deep Reinforcement Learning. Master's, Technische Universität München.

Full text not available from this repository.

Abstract

Considering an increasing urbanization on a global scale, traffic congestion is becoming a progressively larger problem. At the same time, new communication standards enable an interconnectedness of vehicles, which can be used to realize highly automated driving. Highly automated driving allows little distances to the preceding vehicle, thereby increasing the road capacity and decreasing the energy consumption. Artificial intelligence made great advances in recent time and can now be used for ambitious control problems. In this work, the usability of deep Reinforcement Learning for platooning control is examined and the possibility of energy minimization whilst still ensuring operational safety is investigated. Alongside string stability, also robustness against burst errors in communication is incorporated. The obtained controllers are verified with respect to given specifications and validated by comparison with a model-based controller.

Item URL in elib:https://elib.dlr.de/128429/
Document Type:Thesis (Master's)
Title:Platooning Control with Deep Reinforcement Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mirwald, JonasUNSPECIFIEDUNSPECIFIED
Date:14 June 2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:114
Status:Published
Keywords:Reinforcement learning, deep learning, artifical intelligence, neural network, control, platooning, adaptive cruise control, cooperative adaptive cruise control
Institution:Technische Universität München
Department:Department of Informatics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - NGC KoFiF
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of System Dynamics and Control > Vehicle System Dynamics
Deposited By: Mirwald, Jonas
Deposited On:15 Jul 2019 14:06
Last Modified:17 Dec 2019 15:05

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