Measuring, modelling and minimizing perceived motion incongruence for vehicle motion simulation
Diane Cleij
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Diane Cleij, Measuring, modelling and minimizing perceived motion incongruence for vehicle motion simulation (2020), Logos Verlag, Berlin, ISBN: 9783832587307
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Beschreibung / Abstract
Humans always wanted to go faster and higher than their own legs could carry them. This led them to invent numerous types of vehicles to move fast over land, water and air. As training how to handle such vehicles and testing new developments can be dangerous and costly, vehicle motion simulators were invented.
Motion-based simulators in particular, combine visual and physical motion cues to provide occupants with a feeling of being in the real vehicle. While visual cues are generally not limited in amplitude, physical cues certainly are, due to the limited simulator motion space. A motion cueing algorithm (MCA) is used to map the vehicle motions onto the simulator motion space. This mapping inherently creates mismatches between the visual and physical motion cues.
Due to imperfections in the human perceptual system, not all visual/physical cueing mismatches are perceived. However, if a mismatch is perceived, it can impair the simulation realism and even cause simulator sickness. For MCA design, a good understanding of when mismatches are perceived, and ways to prevent these from occurring, are therefore essential.
In this thesis a data-driven approach, using continuous subjective measures of the time-varying Perceived Motion Incongruence (PMI), is adopted. PMI in this case refers to the effect that perceived mismatches between visual and physical motion cues have on the resulting simulator realism. The main goal of this thesis was to develop an MCA-independent off-line prediction method for time-varying PMI during vehicle motion simulation, with the aim of improving motion cueing quality.
To this end, a complete roadmap, describing how to measure and model PMI and how to apply such models to predict and minimize PMI in motion simulations is presented. Results from several human-in-the-loop experiments are used to demonstrate the potential of this novel approach.
Motion-based simulators in particular, combine visual and physical motion cues to provide occupants with a feeling of being in the real vehicle. While visual cues are generally not limited in amplitude, physical cues certainly are, due to the limited simulator motion space. A motion cueing algorithm (MCA) is used to map the vehicle motions onto the simulator motion space. This mapping inherently creates mismatches between the visual and physical motion cues.
Due to imperfections in the human perceptual system, not all visual/physical cueing mismatches are perceived. However, if a mismatch is perceived, it can impair the simulation realism and even cause simulator sickness. For MCA design, a good understanding of when mismatches are perceived, and ways to prevent these from occurring, are therefore essential.
In this thesis a data-driven approach, using continuous subjective measures of the time-varying Perceived Motion Incongruence (PMI), is adopted. PMI in this case refers to the effect that perceived mismatches between visual and physical motion cues have on the resulting simulator realism. The main goal of this thesis was to develop an MCA-independent off-line prediction method for time-varying PMI during vehicle motion simulation, with the aim of improving motion cueing quality.
To this end, a complete roadmap, describing how to measure and model PMI and how to apply such models to predict and minimize PMI in motion simulations is presented. Results from several human-in-the-loop experiments are used to demonstrate the potential of this novel approach.
Inhaltsverzeichnis
- BEGINN
- 1 Introduction
- 1.1 Motion cueing algorithms
- 1.2 Cueing quality
- 1.3 Research Goals
- 1.4 Approach
- 1.5 Scope
- 1.6 Outline
- I Continuous Rating of Perceived Motion Incongruence
- 2 Continuous Subjective Rating of Perceived Motion Incongruence
- 2.1 Introduction
- 2.2 Continuous Rating Method
- Reliability
- Validity
- Applicability
- 2.3 Experiment
- 2.4 Results
- 2.5 Discussion
- 2.6 Conclusion
- 3 Comparison filter- and optimization-based MCA
- 3.1 Introduction
- 3.2 Motion cueing algorithms
- 3.3 Methods
- 3.4 Results
- Global scaling
- Washout
- Tilt-coordination
- Prepositioning
- Velocity buffering
- 3.4.5 Linear acceleration simulation in Daimler simulator
- 3.5 Discussion
- 3.6 Conclusions
- II Modelling Perceived Motion Incongruence
- 4 Cueing Error Detection Algorithm
- 4.1 Introduction
- 4.2 Algorithm
- Non binary shape and gain measures
- 4.2.3 Mathematical Cueing Error Definitions
- Shape Measure
- Semblance
- Relevant semblance
- Averaging and smoothing
- Shape Measure Threshold
- Gain Measure
- 4.2.6 Algorithm Outcome
- 4.3 Motion Profiles
- 4.4 Results
- 4.5 Discussion
- 4.6 Conclusion
- 5 Modelling Perceived Motion Incongruence
- 5.1 Introduction
- 5.2 The Model
- Non-Linear Part
- Linear Part
- 5.2.4 Model Parameters and Choices
- 5.3 System Identification Process
- Selection Criteria
- Avoid Negative Input Contributions (ANIC)
- 5.3.3 Step 4: Parameter Estimation
- Goodness of fit
- Negative input contributions
- Residuals
- Uncertainty Analysis
- 5.3.5 SI Process Parameters
- 5.4 Analysis
- Basic
- Additional Inputs
- Including CEDA
- 5.4.2 Datasets
- Model Structure Analysis
- Model Explanatory Analysis
- Model Prediction Analysis
- 5.5 Results
- 5.6 Discussion
- 5.7 Conclusion
- 6 Model Transfer Between Experiments
- 6.1 Introduction
- 6.2 Experiments
- 6.3 Model Transfer Parameter
- 6.4 Results
- CMS Data Results
- Daimler Data Results
- 6.4.3 Combined Experiment Fitting
- 6.5 Discussion
- 6.6 Conclusions
- III Minimizing Perceived Motion Incongruence
- 7 Optimizing Motion Cueing with a MIR Model
- 7.1 Introduction
- 7.2 MPC-based MCA
- Perception-based Weights
- 7.2.3 MCA Output
- Predicted MIR
- 7.3 Experiment
- 7.4 Results
- Overall Rating
- Continuous Rating
- 7.4.3 Participant Groups
- 7.5 Discussion
- 7.6 Conclusion
- 8 Conclusions and Recommendations
- 8.1 Measuring Perceived Motion Incongruence
- 8.2 Modelling Perceived Motion Incongruence
- 8.3 Minimizing Perceived Motion Incongruence
- 8.4 Recommendations
- A The Model: MIR Averaging
- B The Model: Optional Non-linear Subsystems
- B.1 Rotational Rate
- B.2 Rotational Angle
- C SI Process: Estimating Criteria Thresholds
- C.1 Synthetic Datasets
- C.2 Results
- C.3 Threshold Choice
- D SI Process: Selection Criteria Parameters
- D.1 Influence of W
- D.2 Influence of ANIC
- E SI Process: Parameter Estimation
- E.1 Maximum Likelihood Estimation
- E.2 Prediction Error Method for ARX model structures
- E.3 Influence of non-IDD noise
- F MTP Estimation
- F.1 Serial
- F.2 Parallel
- F.3 Discussion and Conclusion
- G Model Structure, Residual and Uncertainty Analysis
- G.1 CMS Data Results
- G.2 Daimler Data Results
- G.3 Combined Experiment Fitting
- H Questionnaire
- Acknowledgements
- Curriculum Vità¦
- List of Publications