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.

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