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, -.
Cleij D, Venrooij J, Pretto P, Pool DM, Mulder M and Bülthoff HH (September-2017) Continuous Subjective Rating of Perceived Motion Incongruence During Driving Simulation IEEE Transactions on Human-Machine Systems Epub ahead.
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.

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Motion Perception and Simulation

The subjective experience of locomotion, i.e. the displacement of a human observer through the environment, is what we call self-motion. To fully comprehend this pervasive experience, we take a two-fold approach: on the one hand, we carry out fundamental research to investigate human perception of self-motion; on the other hand, we develop state-of-the-art motion simulation technologies and algorithms. Ultimately, these two research directions build upon each other [20]: the more detailed our knowledge on the mechanisms behind perception, the higher the fidelity that can be achieved in simulations; and the more realistic the simulations, the more advanced research can be conducted on motion perception.

Research Goals

Our fundamental research investigates both the low-level processes of uni- and multi-sensory visual/inertial motion perception, and the high-level abstract representations of self-motion, including the conscious experience of, and cognitive response to it. Low-level research allow us to describe the relation between actual and perceived motion characteristics [4,5,12,13,15,16]; whereas through high-level research we can better understand the causes of motion sickness [18], predict the subjective assessment of motion simulation fidelity [1,2,20,21], and improve the operator performance of remotely controlled vehicles [10,11]. Our applied research on simulation technologies aims at developing ecologically valid virtual environments to achieve a high fidelity simulation of self-motion. First, we work on the creation of visual environments that are used in our experiments or as development tools. Second, we explore ways to make optimal use of a motion simulator's capabilities to provide the simulator user with a realistic motion experience while accounting for the physical limits of the simulator [8,9,19].

Methods and Facilities

  • MPI CybmerMotion Simulator
  • MPI CableRobot Simulator

In our low-level research on motion perception, we present participants with elementary motion stimuli using various (adaptive) psychophysical paradigms. Research on perceptual thresholds typically involves n-Alternative Forced Choice tasks, in which participants select one of n sequentially presented motion stimuli on the basis of a criterion of interest [4-7,12-17]. In addition, we are using functional Near-InfraRed Spectroscopy (fNIRS) as a means to obtain direct readings of cortical hemodynamic activity, which may provide an additional dimension of experimental evidence [7]. In our high-level research, we seek to determine qualities of conscious experience, such as perceived simulation fidelity and workload, for complex scenarios with high ecological validity. As stimuli, we present for example virtual driving/flying scenarios, and we have 'played back' visual-inertial recordings of actual car driving and helicopter flight. For data collection in these experiment we have adopted questionnaires, we have adapted magnitude estimation methods and developed new ones (i.e., 'continuous rating') [1-3,20-23].

For the majority of our studies, we rely on the Opens internal link in current windowMPI CyberMotion Simulator, a dynamic motion platform for immersive virtual environments and vehicle simulation. We also operate the Opens internal link in current windowMPI CableRobot Simulator, the world's first cable robot for passengers. Furthermore, we use the CyberPod, a mid-size hexapod motion simulator. These platforms provide a range of possibilities in terms of inertial stimulation, and are often used in combination with a variety of visualization tools to provide immersive virtual environments. For work on visual motion we also make use of the PanoLab, the BackPro and purpose-built VR setups.

Selected Results

Figure 1: Map of the probability that the perceived direction of self-motion will be based upon both visual and physical stimulation, or whether visual and physical stimulation will be processed separately, as a function of the similarity between the signals [5].
Figure 2: A model of the MPI CyberMotion Simulator and a representation of the forces (red) acting on the participant's head inside the simulator as obtained from QVis, overlayed with recordings of hemodynamic activity (yellow), as obtained from QBrain.

We have identified functions that relate the perceived characteristics of motion stimuli to their actual characteristics, such as velocity, acceleration, and heading [4,5,6,12,13], and we have determined absolute and differential thresholds for yaw, roll, and heading [7,14-16].

We have investigated how perceptions of any particular characteristic are affected by the presence of other stimuli [15-17] and how multiple sensory systems interact [5,6] (Figure 1).

We have determined how visual motion contributes to the illusory perception of physical motion (vection) and how it might explain motion sickness [18].

We have measured the perceived quality (visual/inertial motion incongruence) of motion cueing solutions and created several models to effectively predict it [2,20,21,23].

We have shown how teleoperation performance benefit from the addition of a motion feedback channel, informing the operator about the vehicle state and task-relevant motion [10,11].

We have developed software tools for various research projects on motion simulation. In particular, we have created an Off-line Motion Simulation Framework (OMSF), which is used to calculate offline control inputs (optimal motion trajectories) for any motion simulator, given its dynamics and constraints, and predefined trajectories.

We have also created a software library that serves as template for model-predictive control (tmpc) applications [8,9]. Implementations of both these software are currently in use in all our simulators [1,3,9,22].

We have developed Opens internal link in current windowQVis, which is a tool to visualize motion trajectories and motion cueing solutions in different simulators (Figure 2). This tool was used in several collaborations with industrial partners. QVis has also been adapted into QBrain for visualization of cortical hemodynamics from fNIRS-data (Figure 2).

We have designed, implemented and tested a 'motion teleoperation' setup, to control an unmanned aerial vehicle from within a motion simulator, which creates additional feedback using motion information in addition to the usual visual feedback [10,11].

We have also designed a 'theoretical driving simulator' [19], which can be used to find the minimal requirements for a simulator to replicate any desired trajectory.

We have recently built what we call an 'Alternative Reality' system, with the goal of manipulating our participants' actual surroundings with the highest possible degree of ecological validity.

Collaborations

  • Opens internal link in current windowCybernetics Approach to Perception and Action
  • Opens internal link in current windowCognition & Control in Human-Machine Systems
  • Jean-Pierre Bresciani - Université de Fribourg (Fribourg, Switzerland)
  • Moritz Diehl - Universität Freiburg (Freiburg, Germany)
  • Heiko Hecht - Johannes Gutenberg-Universität Mainz (Mainz, Germany)
  • Max Mulder - Technische Universiteit Delft (Delft, the Netherlands)
  • Fabio Solari - Università Degli Studi Di Genova (Genoa, Italy)
  • Andreas Zell - Universität Tübingen (Tübingen, Germany)
  • Hasan Ayaz - Drexel University (Philadelphia, PA, USA)

Publications (2015-2017)

  1. Cleij D, Venrooij J, Pretto P, Katliar M, Bülthoff HH, Steffen B, Hoffmeyer FW and Schöner H-P (2017) Comparison between filter- and optimization-based motion cueing algorithms for driving simulation. Transportation Research Part F: Traffic Psychology and Behaviour (Epub ahead)
  2. Cleij D, Venrooij J, Pretto P, Pool DM, Mulder M and Bülthoff HH (2017) Continuous subjective rating of perceived motion incongruence during driving simulation. IEEE Transactions on Human-Machine Systems (in press)
  3. Cleij D, Venrooij J, Pretto P, Pool DM, Mulder M and Bülthoff HH (September 2015) Continuous rating of perceived visual-inertial motion incoherence during driving simulation. DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 191-198
  4. de Winkel KN, Katliar M and Bülthoff HH (2015) Forced fusion in multisensory heading estimation. PLoS ONE 10(5) 1-20
  5. de Winkel KN, Katliar M and Bülthoff HH (2017) Causal inference in multisensory heading estimation. PLoS ONE 12(1) 1-20
  6. de Winkel KN, Katliar M and Bülthoff HH (September 2015) Heading coherence zone from causal inference modelling. DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 67-70
  7. de Winkel KN, Nesti A, Ayaz H and Bülthoff HH (2017) Neural correlates of decision making on whole body yaw rotation: an fNIRS study. Neuroscience Letters 654 56-62
  8. Katliar M, de Winkel KN, Venrooij J, Pretto P and Bülthoff HH (September 2015) Impact of mpc prediction horizon on motion cueing fidelity. DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 219-222
  9. 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)
  10. Lächele J, Venrooij J, Pretto P and Bülthoff HH (May 2016) Effects of vehicle- and task-related motion feedback on operator performance in teleoperation. Leveraging Emerging Technologies for Future Capabilities, 72nd American Helicopter Society International Annual Forum (AHS 2016), Curran, Red Hook, NY, USA, 3310-3316
  11. Lächele J, Venrooij J, Pretto P, Zell A and Bülthoff HH (September 2015) Novel approach for calculating motion feedback in teleoperation. 7th European Conference on Mobile Robots (ECMR 2015), IEEE, Piscataway, NJ, USA, 1-6
  12. Nesti A, Beykirch KA, Pretto P and Bülthoff HH (2015) Human discrimination of head-centred visual-inertial yaw rotations. Experimental Brain Research 233(12) 3553-3564
  13. Nesti A, Beykirch KA, Pretto P and Bülthoff HH (2015) Self-motion sensitivity to visual yaw rotations in humans. Experimental Brain Research 233(3) 861-869
  14. Nesti A, de Winkel KN and Bülthoff HH (2017) Accumulation of inertial sensory information in the perception of whole body yaw rotation. PLoS ONE 12(1) 1-14
  15. Nesti A, Nooij SAE, Losert M, Bülthoff HH and Pretto P (2016) Roll rate perceptual thresholds in active and passive curve driving simulation. Simulation: Transactions of the Society for Modeling and Simulation International 92(5) 417-426
  16. Nooij SAE, Nesti A, Bülthoff HH and Pretto P (2016) Perception of rotation, path, and heading in circular trajectories. Experimental Brain Research 234(8) 2323-2337
  17. Nooij SAE, Pretto P and Bülthoff HH (September 2015) Sensitivity to lateral force is affected by concurrent yaw rotation during curve driving. DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 33-38
  18. Nooij SAE, Pretto P, Oberfeld D, Hecht H and Bülthoff HH (2017) Vection is the main contributor to motion sickness induced by visual yaw rotation: implications for conflict and eye movement theories. PLoS ONE 12(4) 1-19
  19. Olivari M, Pretto P, Venrooij J and Bülthoff HH (to be submitted) Defining the kinematic requirements for a theoretical driving simulator. Transportation Research Part F: Traffic Psychology and Behaviour
  20. Pretto P, Venrooij J, Nesti A, Bülthoff HH (2015) Perception-Based Motion Cueing: A Cybernetics Approach to Motion Simulation In: Recent Progress in Brain and Cognitive Engineering (Ed) Lee, S.-W. , H.H. Müller, K.-R. Müller Springer, Dordrecht, The Netherlands, 131-152
  21. van Leeuwen TD, Cleij D, Pool DM, Mulder M and Bülthoff HH (to be submitted) Time-varying perceived visual-motion mismatch due to lateral specific force scaling during passive curve driving simulation. Transportation Research Part F: Traffic Psychology and Behaviour
  22. Venrooij J, Cleij D, Katliar M, Pretto P, Bülthoff HH, Steffen D, Hoffmeyer FW and Schöner H-P (September 2016) Comparison between filter- and optimization-based motion cueing in the daimler driving simulator. DSC 2016 Europe: Driving Simulation Conference & Exhibition, 31-38
  23. Venrooij J, Pretto P, Katliar M, Nooij SAE, Nesti A, Lächele M, de Winkel KN, Cleij D and Bülthoff HH (September 2015) Perception-based motion cueing: validation in driving simulation. DSC 2015 Europe: Driving Simulation Conference & Exhibition, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 153-161
For publications prior to 2015 please visit the Opens internal link in current windowPublications page of the department.
Last updated: Tuesday, 25.07.2017