Modeling Coherence Zones in Flight Simulation During Yaw Motion

Buinhas, Luisa dos Santos; Grácio, B.J. Correia; Pais, A.R. Valente; Paassen, Marinus van

doi:10.2514/6.2013-5223

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…This signal-dependent noise characteristic was measured from experimental data in [12], and it is consistent with perception tests carried out in the literature [24][25][26][27][28]. However, these studies generally focussed on sinusoidal stimuli with a single frequency.…”

Section: Discussionsupporting

confidence: 82%

A Simulation Study of Human Sensory Dynamics and Driver–Vehicle Response

Nash

Cole

2022

Journal of Dynamic Systems, Measurement, and Control

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In previous work a driver model with sensory dynamics (visual and vestibular) was developed, identified and validated, using data from moving-base driving simulator experiments. In the present paper the predictions, applications and limitations of the driver model are explored through a series of simulations. The visual system is found to be the dominant sensory system, with the influence of vestibular measurements increasing with the proportion of random disturbances on the vehicle. State perception errors increase significantly with the proportion of random disturbances on the vehicle. The driver's simulated control performance is unchanged with signal amplitude above perception threshold levels, although it is slightly affected by high-pass filtering of the physical motion such as might be experienced in a driving simulator. The sensory driver model led to a significantly different optimum value of vehicle centre of mass position compared to that obtained using an idealised driver model. The results motivate the adoption of sensory driver models in a vehicle design setting. Further work could be undertaken to improve the sensorimotor noise model.

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Section: Discussionsupporting

confidence: 82%

A Simulation Study of Human Sensory Dynamics and Driver–Vehicle Response

Nash

Cole

2022

Journal of Dynamic Systems, Measurement, and Control

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“…In other words, the width of the band for which Pr(C | e V , e I ) is relatively high is predicted by the sum of the sensory variances. This theoretical result corresponds to empirical findings on the width of the CZ for yaw rotations [45]. Fig.…”

Section: Multisensory Distance Estimationsupporting

confidence: 90%

“…The concept was derived from the observation that physically moving observers are unable to detect concurrent visual motion when the amplitude of the visual motion is below a particular threshold [39,40]. CZ have been determined for the amplitude and phase of rotational as well as translation motion [41,42,43,44,45], and also for heading [46,30]. CI models can be said to be formed around an analogous concept, namely the probability that visual and inertial motion with any discrepancy will be attributed to a common cause Pr(C | e V , e I ).…”

Section: Multisensory Distance Estimationmentioning

confidence: 99%

An assessment of Causal Inference in visual-inertial traveled distance estimation

Winkel

Diers

Lächele

et al. 2018

Preprint

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Recent work indicates that the central nervous system assesses the causality of visual and inertial information in the estimation of qualitative characteristics of self-motion and spatial orientation, and forms multisensory perceptions in accordance with the outcome of these assessments.Here, we extend the assessment of this Causal Inference (CI) strategy to the quantitative domain of traveled distance. We present a formal model of how stimuli result in sensory estimates, how percepts are constructed from sensory estimates, and how responses result from percepts. Starting with this formalization, we derived probabilistic formulations of CI and competing models for perception of traveled distance. In an experiment, participants (n=9) were seated in the Max Planck Cablerobot Simulator, and shown a photo-realistic virtual rendering of the simulator hall via a Head-Mounted Display. Using this setup, the participants were presented with various unisensory and (incongruent) multisensory visual-inertial horizontal linear surge motions, differing only in amplitude (i.e., traveled distance). Participants performed both a Magnitude Estimation and a Two-Interval Forced Choice task. Overall, model comparisons favor the CI model, but individual analysis shows a Cue Capture strategy is preferred in most individual cases. Parameter estimates indicate that visual and inertial sensory estimates follow a Stevens' power law with positive exponent, and that noise increases with physical distance in accordance with a Weber's law. Responses were found to be biased towards the mean stimulus distance, consistent with an interaction between percepts and prior knowledge in the formulation of responses. Magnitude estimate data further showed a regression to the mean effect. The experimental data did not provide unambiguous support for the CI model. However, model derivations and fit results demonstrate it can reproduce empirical findings, arguing in favor of the CI model. Moreover, the methods outlined in the present study demonstrate how different sources of distortion in responses may be disentangled by combining psychophysical tasks.

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A review of human sensory dynamics for application to models of driver steering and speed control

2016

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In comparison with the high level of knowledge about vehicle dynamics which exists nowadays, the role of the driver in the driver–vehicle system is still relatively poorly understood. A large variety of driver models exist for various applications; however, few of them take account of the driver’s sensory dynamics, and those that do are limited in their scope and accuracy. A review of the literature has been carried out to consolidate information from previous studies which may be useful when incorporating human sensory systems into the design of a driver model. This includes information on sensory dynamics, delays, thresholds and integration of multiple sensory stimuli. This review should provide a basis for further study into sensory perception during driving.

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Modeling Coherence Zones in Flight Simulation During Yaw Motion

Cited by 5 publications

References 40 publications

A Simulation Study of Human Sensory Dynamics and Driver–Vehicle Response

A Simulation Study of Human Sensory Dynamics and Driver–Vehicle Response

An assessment of Causal Inference in visual-inertial traveled distance estimation

A review of human sensory dynamics for application to models of driver steering and speed control

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