Kraft, Edward ; He, Ping ; Rinderknecht, Stephan (2022)
Application of Prepositioning Strategies in a Compensational Motion Cueing Algorithm.
Driving Simulation Conference 2022 Europe VR. Strasbourg (14.09.2022-16.09.2022)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Motion cueing algorithms (MCA) are an essential part of the operation of driving simulators. Challenges in choosing and parametrizing these MCA lie in the accuracy and workspace usage efficiency. In addition, false cues have to be minimized to reduce simulator sickness. So-called compensational MCA try to compensate the washout by tilt coordination. To further improve workspace efficiency, prepositioning strategies seek to predict the driver’s behavior and dynamically prepare the simulator for the upcoming movement. In this paper, dynamic prepositioning strategies are applied to a compensational MCA. Their performances are analyzed for two different driving scenarios. The maximum scaling factor was used as an evaluation parameter for the comparison of the investigated prepositioning strategies. The results show that maximum scaling factors can be slightly increased by applying prepositioning strategies. If keeping the scaling factor constant, tilt activity can be reduced which is assumed to be having a positive impact on simulator sickness. The results indicate that prepositioning strategies show the most robust performance for predefined test runs since the driver’s behavior is known in advance. Contrary to expectation, strong washouts in the compensational MCA are not suitable in combination with prepositioning strategies.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Kraft, Edward ; He, Ping ; Rinderknecht, Stephan |
| Art des Eintrags: | Bibliographie |
| Titel: | Application of Prepositioning Strategies in a Compensational Motion Cueing Algorithm |
| Sprache: | Englisch |
| Publikationsjahr: | 16 November 2022 |
| Ort: | Strasbourg |
| Verlag: | Driving Simulation Association |
| Buchtitel: | Proceedings of the Driving Simulation Conference 2022 Europe VR |
| Veranstaltungstitel: | Driving Simulation Conference 2022 Europe VR |
| Veranstaltungsort: | Strasbourg |
| Veranstaltungsdatum: | 14.09.2022-16.09.2022 |
| URL / URN: | https://proceedings.driving-simulation.org/proceeding/dsc-20... |
| Kurzbeschreibung (Abstract): | Motion cueing algorithms (MCA) are an essential part of the operation of driving simulators. Challenges in choosing and parametrizing these MCA lie in the accuracy and workspace usage efficiency. In addition, false cues have to be minimized to reduce simulator sickness. So-called compensational MCA try to compensate the washout by tilt coordination. To further improve workspace efficiency, prepositioning strategies seek to predict the driver’s behavior and dynamically prepare the simulator for the upcoming movement. In this paper, dynamic prepositioning strategies are applied to a compensational MCA. Their performances are analyzed for two different driving scenarios. The maximum scaling factor was used as an evaluation parameter for the comparison of the investigated prepositioning strategies. The results show that maximum scaling factors can be slightly increased by applying prepositioning strategies. If keeping the scaling factor constant, tilt activity can be reduced which is assumed to be having a positive impact on simulator sickness. The results indicate that prepositioning strategies show the most robust performance for predefined test runs since the driver’s behavior is known in advance. Contrary to expectation, strong washouts in the compensational MCA are not suitable in combination with prepositioning strategies. |
| Freie Schlagworte: | motion cueing, compensational motion cueing algorithm, driver prediction, dynamic prepositioning, workspace usage efficiency |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS) |
| Hinterlegungsdatum: | 23 Nov 2022 06:39 |
| Letzte Änderung: | 23 Nov 2022 06:39 |
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