The MPI CyberMotion Simulator: A Novel Research Platform to Investigate Human Control Behavior

Nieuwenhuizen, Frank M.; Bülthoff, HH

doi:10.5626/jcse.2013.7.2.122

Cited by 23 publications

(14 citation statements)

References 14 publications

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“…In both experiments, we used the Max Planck CyberMotion Simulator [20] to present participants with a large number (600-648) of unisensory visual-only and inertial-only, and multisensory visual-inertial motion stimuli, with headings ranging the full circle, and discrepancies up to ±90° (in the multisensory conditions). For each experimental trial, participants indicated the heading of self-motion using a pointer device.…”

Section: Resultsmentioning

confidence: 99%

“…The stimuli were presented using the Max Planck Institute CyberMotion Simulator facility [20]. Visual stimuli showed movements through a three dimensional environment with a ground plane and limited-lifetime particles, presented stereoscopically using a dual projection system in conjunction with stereo glasses (Infitec ® GmbH, Ulm, Germany, model INFITEC ® Premium Glasses).…”

Section: Methodsmentioning

confidence: 99%

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Causal Inference in Multisensory Heading Estimation

2017

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A large body of research shows that the Central Nervous System (CNS) integrates multisensory information. However, this strategy should only apply to multisensory signals that have a common cause; independent signals should be segregated. Causal Inference (CI) models account for this notion. Surprisingly, previous findings suggested that visual and inertial cues on heading of self-motion are integrated regardless of discrepancy. We hypothesized that CI does occur, but that characteristics of the motion profiles affect multisensory processing. Participants estimated heading of visual-inertial motion stimuli with several different motion profiles and a range of intersensory discrepancies. The results support the hypothesis that judgments of signal causality are included in the heading estimation process. Moreover, the data suggest a decreasing tolerance for discrepancies and an increasing reliance on visual cues for longer duration motions.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Causal Inference in Multisensory Heading Estimation

2017

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“…We now develop an SSID algorithm to estimate G ff and G fb . For each k ∈ N, define σ k ≜ e ȷθ k , and define (10) where…”

Section: Subsystem Identification Algorithmmentioning

confidence: 99%

“…SSID also has application to modeling human behavior. For example, there is interest in modeling human-inthe-loop behavior for applications such as aircraft [7][8][9][10] and automobiles [11][12][13]. In addition, SSID methods can be used to model human behavior in motor control experiments, which study human learning [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Subsystem identification of multivariable feedback and feedforward systems

Zhang

Hoagg

2016

Automatica

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We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input-output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters.

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“…-Robot arm system by Kuka with realization at the Max Planck Institute in Tuebingen (Nieuwenhuizen and B€ ulthoff 2013). Here the motion system is based on a production robot, which is available in large quantities and thus at relatively low cost.…”

Section: Concepts For Dynamic Driving Simulatorsmentioning

confidence: 99%

Dynamic Driving Simulators

Schöner¹,

Morys²

2015

Handbook of Driver Assistance Systems

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Depending on the application, different concepts for driving simulators have been realized. A quite common dynamic driving simulator concept for professional applications, i.e., a motion platform consisting of a hexapod based on a linear rail, is explained in detail. Using Daimler's dynamic simulator as an example, the essential technological components of driving simulators are explained and the potentials and limitations due to the sensitivity of the human vestibular organ are discussed. Reasons for simulator sickness and how to avoid it complete the part on simulator design.A second focus is placed on the design of simulator experiments with test persons. A clear goal of the simulation experiment, a good choice of technical and psychological test design, and knowledge about the behavior of test persons help set up effective driving simulator tests. Driver distraction is an essential feature to simulate the complete scope of real accident situations and to assess the behavior of a representative set of test persons.The factors affecting the validity of simulator experiments compared to real-world experiments are discussed, and some findings on the opportunities and limitations of simulator experiments are presented.1 General Overview of Driving Simulators Applications of Driving SimulatorsDriving simulators are used widely in the automotive industry and in automotive research institutions for many diverse applications, in particular for the following usages (in the order of rising requirements with respect to realism of motion rendering):-Functional vehicle demonstrations, advertising new vehicle functions (demos at auto shows) -Investigations into cabin, display, and control concepts (assessing reachability, clarity, comprehensibility, etc.) -Training for drivers (of emergency vehicles, for fuel-saving driving style, race tracks, etc.) -Accident research (accident reconstructions, driver behavior analyses, etc.) -Study of driver performance and development of driver models (with respect to tiredness, attention, responsiveness, etc.) as a basis for off-line simulations -Testing and verification of driver assistance systems (effectiveness, controllability, statistical analyses, etc.) -Development of chassis systems and vehicle dynamics control systems (analysis of variants, parameter tuning, etc.).All these applications focus on the interaction of human drivers with a vehicle's technical systems, especially in challenging driving situations (with respect to other traffic participants, obstacles, dangers,

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The MPI CyberMotion Simulator: A Novel Research Platform to Investigate Human Control Behavior

Cited by 23 publications

References 14 publications

Causal Inference in Multisensory Heading Estimation

Causal Inference in Multisensory Heading Estimation

Subsystem identification of multivariable feedback and feedforward systems

Dynamic Driving Simulators

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