Self-Learning Longitudinal Control for On-Road Vehicles

dc.contributor.author	Puccetti, Luca
dc.date.accessioned	2023-07-19T09:02:58Z
dc.date.available	2023-07-19T09:02:58Z
dc.date.issued	2023
dc.date.submitted	2023-06-20T10:55:01Z
dc.identifier	https://library.oapen.org/handle/20.500.12657/63614
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/101654
dc.description.abstract	Reinforcement 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.language	English
dc.relation.ispartofseries	Karlsruher Beiträge zur Regelungs- und Steuerungstechnik
dc.rights	open access
dc.subject.other	Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning
dc.subject.other	thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
dc.title	Self-Learning Longitudinal Control for On-Road Vehicles
dc.type	book
oapen.identifier.doi	10.5445/KSP/1000156966
oapen.relation.isPublishedBy	68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages	158
dc.seriesnumber	20

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