Self-Learning Longitudinal Control for On-Road Vehicles
| dc.contributor.author | Puccetti, Luca | |
| dc.date.accessioned | 2023-06-20T10:55:01Z | |
| dc.date.available | 2023-06-20T10:55:01Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/63614 | |
| 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. | en_US |
| dc.language | English | en_US |
| dc.relation.ispartofseries | Karlsruher Beiträge zur Regelungs- und Steuerungstechnik | en_US |
| dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering | en_US |
| dc.subject.other | Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning | en_US |
| dc.title | Self-Learning Longitudinal Control for On-Road Vehicles | en_US |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000156966 | en_US |
| oapen.relation.isPublishedBy | 44e29711-8d53-496b-85cc-3d10c9469be9 | en_US |
| oapen.series.number | 20 | en_US |
| oapen.pages | 158 | en_US |
| peerreview.anonymity | All identities known | |
| peerreview.id | 51a542ec-eaeb-47c2-861d-6022e981a97a | |
| peerreview.open.review | No | |
| peerreview.publish.responsibility | Books or series editor | |
| peerreview.review.stage | Pre-publication | |
| peerreview.review.type | Full text | |
| peerreview.reviewer.type | Editorial board member | |
| peerreview.reviewer.type | External peer reviewer | |
| peerreview.title | Dissertations in Series (Dissertationen in Schriftenreihe) |
