Self-Learning Longitudinal Control for On-Road Vehicles


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Author(s)
Puccetti, Luca
Language
English
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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.
URI
https://library.oapen.org/handle/20.500.12657/63614
Keywords
Regelungstechnik; Künstliche Intelligenz; Fahrzeugregelung; Längsdynamik; Bestärkendes Lernen; Control Theory; Artificial Intelligence; Vehicle Control; Longitudinal Dynamics; Reinforcement Learning
DOI
10.5445/KSP/1000156966
ISBN
9783731512905
Publisher
KIT Scientific Publishing
Publisher website
https://www.ksp.kit.edu/index.php?link=shop&sort=all
Publication date and place
2023
Series
Karlsruher Beiträge zur Regelungs- und Steuerungstechnik, 20
Classification
Electrical engineering
Pages
158
Rights
https://creativecommons.org/licenses/by/4.0/