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Self-Learning Longitudinal Control for On-Road Vehicles

dc.contributor.authorPuccetti, Luca
dc.date.accessioned2025-03-08T03:08:27Z
dc.date.available2025-03-08T03:08:27Z
dc.date.issued2023
dc.date.submitted2023-06-20T10:55:01Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/63614
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/176864
dc.description.abstractReinforcement Learning is a promising tool to automate controller tuning. However, significant extensions are required for real-world applications to enable fast and robust learning. This work proposes several additions to the state of the art and proves their capability in a series of real world experiments.
dc.languageEnglish
dc.relation.ispartofseriesKarlsruher Beiträge zur Regelungs- und Steuerungstechnik
dc.rightsopen access
dc.subject.otherRegelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning
dc.subject.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
dc.titleSelf-Learning Longitudinal Control for On-Road Vehicles
dc.typebook
oapen.identifier.doi10.5445/KSP/1000156966
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages158
dc.seriesnumber20
dc.anonymityAll identities known
dc.peerreviewid51a542ec-eaeb-47c2-861d-6022e981a97a
dc.peerreviewtitleDissertations in Series (Dissertationen in Schriftenreihe)
dc.openreviewNo
dc.responsibilityBooks or series editor
dc.stagePre-publication
dc.reviewtypeFull text
dc.reviewertypeEditorial board member
dc.reviewertypeExternal peer reviewer


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