A Framework for Robust Remote Driving Strategy Selection

Michael Klöppel-Gersdorf, Thomas Otto

2022

Abstract

In this paper, a framework for assisting Connected Vehicle (CV) is proposed, with the goal of generating optimal parameters for existing driving functions, e.g., parking assistant or Adaptive Cruise Control (ACC), to allow the CV to move autonomously in restricted scenarios. Such scenarios encompass yard automation as well as valet parking. The framework combines Model predictive control (MPC) with particle filter estimators and robust optimization.

Paper Citation

in Harvard Style

Klöppel-Gersdorf M. and Otto T. (2022). A Framework for Robust Remote Driving Strategy Selection. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-573-9, pages 418-424. DOI: 10.5220/0011088900003191

in Bibtex Style

@conference{vehits22,
author={Michael Klöppel-Gersdorf and Thomas Otto},
title={A Framework for Robust Remote Driving Strategy Selection},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2022},
pages={418-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011088900003191},
isbn={978-989-758-573-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - A Framework for Robust Remote Driving Strategy Selection
SN - 978-989-758-573-9
AU - Klöppel-Gersdorf M.
AU - Otto T.
PY - 2022
SP - 418
EP - 424
DO - 10.5220/0011088900003191