Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators

Thomas Schwarzhuber; Michael Graf; Arno Eichberger

doi:10.1109/IV48863.2021.9575334

Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators

Thomas Schwarzhuber, Michael Graf, Arno Eichberger

Institut für Fahrzeugtechnik (3310)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

State of the art motion cueing algorithms aim
at reproducing a simulated vehicle’s motion at maximum
accuracy, while respecting a given motion envelope. The consid-
eration of human sensory characteristics for motion perception
allows to artificially increase this envelope. Model predictive
control based approaches penalise motion deviation for each
perception channel and consequently minimise every error
individually. However, no effort is made to balance motion
cues across different degrees of freedom. In the motorsport
environment it is mandatory to replicate vehicle character-
istics precisely and in a consistent manner. In consequence,
resulting motion cues should also retain these characteristics.
A minimisation of each tracking error individually does not
meet this requirement which is demonstrated in this work.
Furthermore, this paper presents two cost terms for a model
predictive control based motion cueing algorithm which reduce
the deviation of visual-vestibular incongruences across three
perception channels by up to 35%. A simulation based study
further proved that the individual scaling error of each degree
of freedom remains unaffected.

Originalsprache	englisch
Titel	Proceedings of the 32nd IEEE Intelligent Vehicles Symposium 2021
Herausgeber (Verlag)	IEEE Press
Seiten	49-54
Seitenumfang	6
DOIs	https://doi.org/10.1109/IV48863.2021.9575334
Publikationsstatus	Veröffentlicht - 2021
Veranstaltung	32nd IEEE Intelligent Vehicles Symposium: IV21 - NAGOYA UNIVERSITY, Nagoya, Japan Dauer: 11 Juli 2021 → 15 Juli 2021 https://2021.ieee-iv.org/

Konferenz

Konferenz	32nd IEEE Intelligent Vehicles Symposium
Kurztitel	IV21
Land/Gebiet	Japan
Ort	Nagoya
Zeitraum	11/07/21 → 15/07/21
Internetadresse	https://2021.ieee-iv.org/

Schlagwörter

Driving Simulator
motion cueing
motorsports

Zugriff auf Dokument

10.1109/IV48863.2021.9575334

1 Laufend

DVS: Vehicle Dynamics
Koglbauer, I. V., Lex, C., Shao, L., Semmer, M., Rogic, B., Peer, M., Hackl, A., Sternat, A. S., Schabauer, M., Samiee, S., Eichberger, A., Ager, M., Malić, D., Wohlfahrter, H., Scherndl, C., Magosi, Z. F., Orucevic, F., Puščul, D., Arefnezhad, S., Karoshi, P., Schöttel, C. E., Pandurevic, A., Harcevic, A., Wellershaus, C., Li, H., Mihalj, T., Kanuric, T., Gu, Z., Wallner, D., De Cristofaro, F., Soboleva, K., Nalic, D., Bernsteiner, S., Kraus, H., Zhao, Y., Bodner, J., Bui, D. T., Hirschberg, W., Plöckinger, M. & Khoshnood Sarabi, N.
1/01/11 → …
Projekt: Arbeitsgebiet

Dieses zitieren

@inproceedings{cc5330fc985e413faabd33fe7e6d1ff8,

title = "Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators",

abstract = "State of the art motion cueing algorithms aim at reproducing a simulated vehicle's motion at maximum accuracy, while respecting the motion constraints of a cueing platform. The consideration of human sensory characteristics for motion perception allows to artificially increase this envelope. Model predictive control based approaches penalize motion deviation for each perception channel and consequently minimize every error individually. However, no effort is made to balance motion cues across different degrees of freedom. In the motorsport environment it is essential to replicate vehicle characteristics precisely and consistently. The latter is of particular interest, as an inconsistent replication of cues could easily cause a perceived change of vehicle characteristics for a professional race car driver. In consequence, the motion cues should generally retain specific characteristics of the vehicle reference. A minimization of each tracking error individually does not meet this requirement which is demonstrated in this work. To overcome this limitation, two novel cost functions for a model predictive control based motion cueing algorithm are introduced which reduce the deviation of visual-vestibular incongruences. Across three degrees of freedom a reduction of up to 33 % is achieved while scaling errors and translational workspace utilization are retained at a similar level.",

keywords = "Driving Simulator, motion cueing, motorsports",

author = "Thomas Schwarzhuber and Michael Graf and Arno Eichberger",

year = "2021",

doi = "10.1109/IV48863.2021.9575334",

language = "English",

pages = "49--54",

booktitle = "Proceedings of the 32nd IEEE Intelligent Vehicles Symposium 2021",

publisher = "IEEE Press",

note = "32nd IEEE Intelligent Vehicles Symposium : IV21, IV21 ; Conference date: 11-07-2021 Through 15-07-2021",

url = "https://2021.ieee-iv.org/",

}

TY - GEN

T1 - Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators

AU - Schwarzhuber, Thomas

AU - Graf, Michael

AU - Eichberger, Arno

PY - 2021

Y1 - 2021

N2 - State of the art motion cueing algorithms aim at reproducing a simulated vehicle's motion at maximum accuracy, while respecting the motion constraints of a cueing platform. The consideration of human sensory characteristics for motion perception allows to artificially increase this envelope. Model predictive control based approaches penalize motion deviation for each perception channel and consequently minimize every error individually. However, no effort is made to balance motion cues across different degrees of freedom. In the motorsport environment it is essential to replicate vehicle characteristics precisely and consistently. The latter is of particular interest, as an inconsistent replication of cues could easily cause a perceived change of vehicle characteristics for a professional race car driver. In consequence, the motion cues should generally retain specific characteristics of the vehicle reference. A minimization of each tracking error individually does not meet this requirement which is demonstrated in this work. To overcome this limitation, two novel cost functions for a model predictive control based motion cueing algorithm are introduced which reduce the deviation of visual-vestibular incongruences. Across three degrees of freedom a reduction of up to 33 % is achieved while scaling errors and translational workspace utilization are retained at a similar level.

AB - State of the art motion cueing algorithms aim at reproducing a simulated vehicle's motion at maximum accuracy, while respecting the motion constraints of a cueing platform. The consideration of human sensory characteristics for motion perception allows to artificially increase this envelope. Model predictive control based approaches penalize motion deviation for each perception channel and consequently minimize every error individually. However, no effort is made to balance motion cues across different degrees of freedom. In the motorsport environment it is essential to replicate vehicle characteristics precisely and consistently. The latter is of particular interest, as an inconsistent replication of cues could easily cause a perceived change of vehicle characteristics for a professional race car driver. In consequence, the motion cues should generally retain specific characteristics of the vehicle reference. A minimization of each tracking error individually does not meet this requirement which is demonstrated in this work. To overcome this limitation, two novel cost functions for a model predictive control based motion cueing algorithm are introduced which reduce the deviation of visual-vestibular incongruences. Across three degrees of freedom a reduction of up to 33 % is achieved while scaling errors and translational workspace utilization are retained at a similar level.

KW - Driving Simulator

KW - motion cueing

KW - motorsports

U2 - 10.1109/IV48863.2021.9575334

DO - 10.1109/IV48863.2021.9575334

M3 - Conference paper

SP - 49

EP - 54

BT - Proceedings of the 32nd IEEE Intelligent Vehicles Symposium 2021

PB - IEEE Press

T2 - 32nd IEEE Intelligent Vehicles Symposium

Y2 - 11 July 2021 through 15 July 2021

ER -

Investigations on Model Predictive Control Objectives for Motion Cueing Algorithms in Motorsport Driving Simulators

Abstract

Konferenz

Schlagwörter

Zugriff auf Dokument

Fingerprint

Projekte

DVS: Vehicle Dynamics

Dieses zitieren