Project Leaders

Dr. Paolo Pretto 
Phone: +49 7071 601-644 
Fax: +49 7071 601-616 
paolo.pretto[at]tuebingen.mpg.de

Dr. Ksander de Winkel
Phone: +49 7071 601-643
Fax: +49 7071 601-616 
Opens window for sending emailksander.dewinkel[at]tuebingen.mpg.de

News

July 25, 2017
Updated Group Overview page: research goals, methods, results and publications for the period 2015-2017.
 
February 13, 2017
Two new papers published in PLoS ONE:
 
- Nesti A, de Winkel K, Bülthoff HH (2017) Accumulation of Inertial Sensory Information in the Perception of Whole Body Yaw Rotation.
(Opens external link in new windowPLoS ONE 12(1): e0170497)
 
- de Winkel KN, Katliar M, Bülthoff HH (2017) Causal Inference in Multisensory Heading Estimation.
(Opens external link in new windowPLoS ONE 12(1): e0169676)
 
January 30, 2017
Opens external link in new window27th Oculomotor Meeting - Program
The Program of the 27th Oculomotor meeting (3-4 Feb) is now available for download.

Opens internal link in current windowNews Archive

Five most recent Publications

van Leeuwen TD, Cleij D, Pool DM, Mulder M and Bülthoff HH (September-8-2017) Time-varying perceived visual-motion mismatch due to lateral specific force scaling during passive curve driving simulation, DSC 2017 Europe: Driving Simulation Conference & Exhibition, -.
Olivari M, Pretto P, Venrooij J and Bülthoff HH (September-7-2017) Defining the Kinematic Requirements for a Theoretical Driving Simulator, DSC 2017 Europe: Driving Simulation Conference & Exhibition, -.
de Winkel KN, Nesti A, Ayaz H and Bülthoff HH (July-2017) Neural correlates of decision making on whole body yaw rotation: an fNIRS study Neuroscience Letters 654 56–62.
Katliar M, Fischer J, Frison G, Diehl M, Teufel H and Bülthoff HH (July-2017) Nonlinear Model Predictive Control of a Cable-Robot-Based Motion Simulator, 20th World Congress of the International Federation of Automatic Control (IFAC WC 2017), 10249-10255.
Cleij D, Venrooij J, Pretto P, Katliar M, Bülthoff HH, Steffen B, Hoffmeyer FW and Schöner H-P (May-2017) Comparison between filter- and optimization-based motion cueing algorithms for driving simulation Transportation Research Part F: Traffic Psychology and Behaviour Epub ahead.

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WABS Project

Motion simulation is widely used for various applications, such as training, rehabilitation, product development and research. Recent technological advances have improved the realism and therefore the usefulness of motion simulation considerably. These advances, however, mainly concern technology such as vehicle models, visual rendering, control loading and quality of the auditory stimuli. One aspect of motion simulation that has not benefited equally from technological advances, and still remains a main challenge when using a motion simulator, is the motion cueing.

Motion cueing is the process of converting a desired physical motion into commands that are sent to the motion simulator. This conversion is done by an algorithm, called a motion cueing algorithm (MCA). Over the decades, many different types of MCAs have been introduced. The vast majority of them are derivatives of the original approach, which is now widely known as the "classical approach". This classical approach relies mainly on scaling down and filtering the physical motion such that the result "fits" within the limited motion space of a motion simulator.

Within the WABS-project ("Wahrnehmungsbasierte Bewegungssimulation") we developed an alternative approach to motion cueing. This approach is based on the idea that motion cueing can be improved by including novel insights in human self-motion perception, obtained from fundamental motion perception studies, in the MCAs. This "perception-based motion cueing" (PBMC) approach allows for exploiting the limitations and ambiguities of the human perceptual system. PBMC differs from the traditional approach of motion cueing in several ways. The most important difference is that PBMC aims to reproduce the perception of motion, instead of reproducing physical motion. Another important difference is that PBMC operates through optimizing simulator input commands, instead of filtering the motion that is to be reproduced.

Within the WABS project we developed and experimentally tested several PBMC algorithms. Next to the implementation of an advanced perception model, the work included the development of cost functions and the development of experimental methods that allow for reliably measuring the quality of an MCA. With those methods, we were able to experimentally show that PBMC algorithms are superior to the classical approach.

Last updated: Tuesday, 25.07.2017