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Authors: Michael Klöppel-Gersdorf and Thomas Otto

Affiliation: Fraunhofer IVI, Fraunhofer Institute for Transportation and Infrastructure Systems, Dresden, Germany

Keyword(s): Driving Strategy Selection, Yard Automation, Particle Filter, Robust Optimization, Valet Parking, V2X, IEEE 802.11p.

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.

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-573-9; ISSN 2184-495X, SciTePress, pages 418-424. DOI: 10.5220/0011088900003191

@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 - VEHITS},
year={2022},
pages={418-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011088900003191},
isbn={978-989-758-573-9},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - A Framework for Robust Remote Driving Strategy Selection
SN - 978-989-758-573-9
IS - 2184-495X
AU - Klöppel-Gersdorf, M.
AU - Otto, T.
PY - 2022
SP - 418
EP - 424
DO - 10.5220/0011088900003191
PB - SciTePress

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