Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System

Or Yarom, Jannis Fritz, Florian Lange, Xiaobo Liu-Henke

2022

Abstract

In this paper, the systematic model-based design of a reinforcement learning-based neuronal adaptive cruise control is described. Starting with an introduction and a summary of current fundamentals, design methods for intelligent driving functions are presented. The focus is on the first-time presentation of a novel design methodology for artificial neural networks in control engineering. This methodology is then applied and fully validated using the example of an adaptive cruise control system.

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Paper Citation


in Harvard Style

Yarom O., Fritz J., Lange F. and Liu-Henke X. (2022). Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 889-896. DOI: 10.5220/0010923300003116


in Bibtex Style

@conference{icaart22,
author={Or Yarom and Jannis Fritz and Florian Lange and Xiaobo Liu-Henke},
title={Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={889-896},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010923300003116},
isbn={978-989-758-547-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System
SN - 978-989-758-547-0
AU - Yarom O.
AU - Fritz J.
AU - Lange F.
AU - Liu-Henke X.
PY - 2022
SP - 889
EP - 896
DO - 10.5220/0010923300003116