THE GEOMETRIC FIELD OF VIEW AND SPEED PERCEPTION IN A DRIVING SIMULATOR

Particularly in the health and rehabilitation sector where cost and space are constraints, practitioners need smaller driving simulators. Because these small-footprint driving simulators have a limited projected field of view (PFOV) it is desirable to extend the virtual or geometric field of view (GFOV) beyond that natively afforded by the PFOV. Changing the PFOV/GFOV ratio has been shown to alter perceived speed. In order for driving simulation to produce realistic experiences, drivers‟ perception of speed should correspond with real world experiences. The purpose of the current research was to better understand the relationship between speed perception and the GFOV/PFOV ratio in a way that would be useful to simulation practitioners using a small-footprint driving simulator. Using the DS-250, a small-footprint simulator, participants performed a speed matching task using six different GFOV conditions while the PFOV was held constant. Target speeds were presented in three appropriate simulated environments: 25mph in a residential area, 45mph in a commercial area, and 65mph on a freeway. In general, perceived speed was found to decrease with larger GFOVs. However, no GFOV tested produced accurate speed perception; on average, all participants underestimated their speeds using all GFOVs. A regression was used to estimate at which GFOV average error in speed production would approach zero. Subjective data regarding participant strategy, perceived accuracy, and their awareness of different GFOV conditions were also collected

Modelling visual-vestibular integration and behavioural adaptation in the driving simulator

It is well established that not only vision but also other sensory modalities affect drivers’ control of their vehicles, and that drivers adapt over time to persistent changes in sensory cues (for example in driving simulators), but the mechanisms underlying these behavioural phenomena are poorly understood. Here, we consider the existing literature on how driver steering in slalom tasks is affected by down-scaling of vestibular cues, and propose, for the first time, a computational model of driver behaviour that can, based on neurobiologically plausible mechanisms, explain the empirically observed effects, namely: decreased task performance and increased steering effort during initial exposure, followed by a partial reversal of these effects as task exposure is prolonged. Unexpectedly, the model also reproduced another previously unexplained empirical finding: a local optimum for motion down-scaling, where path-tracking is better than when one-to-one motion cues are available. Overall, our findings suggest that: (1) drivers make direct use of vestibular information as part of determining appropriate steering actions, and (2) motion down-scaling causes a yaw rate underestimation phenomenon, where drivers behave as if the simulated vehicle is rotating more slowly than it is. However, (3) in the slalom task, a certain degree of such underestimation brings a path-tracking performance benefit. Furthermore, (4) behavioural adaptation in simulated slalom driving tasks may occur due to (a) down-weighting of vestibular cues, and/or (b) increased sensitivity in timing and magnitude of steering corrections, but (c) seemingly not in the form of a full compensatory rescaling of the received vestibular input. The analyses presented here provide new insights and hypotheses about simulated driving and simulator design, and the developed models can be used to support research on multisensory integration and behavioural adaptation in both driving and other task domains

Perceptual Model-Driven Authoring of Plausible Vibrations from User Expectations for Virtual Environments

One of the central goals of design is the creation of experiences that are rated favorably in the intended application context. User expectations play an integral role in tactile product quality and tactile plausibility judgments alike. In the vibrotactile authoring process for virtual environments, vibra-tion is created to match the user’s expectations of the presented situational context. Currently, inefficient trial and error approaches attempt to match expectations implicitly. A more efficient, model-driven procedure based explicitly on tactile user expectations would thus be beneficial for author-ing vibrations. In everyday life, we are frequently exposed to various whole-body vibrations. Depending on their temporal and spectral proper-ties we intuitively associate specific perceptual properties such as “tin-gling”. This suggests a systematic relationship between physical parame-ters and perceptual properties. To communicate with potential users about such elicited or expected tactile properties, a standardized design language is proposed. It contains a set of sensory tactile perceptual attributes, which are sufficient to characterize the perceptual space of vibration encountered in everyday life. This design language enables the assessment of quantita-tive tactile perceptual specifications by laypersons that are elicited in situational contexts such as auditory-visual-tactile vehicle scenes. Howev-er, such specifications can also be assessed by providing only verbal de-scriptions of the content of these scenes. Quasi identical ratings observed for both presentation modes suggest that tactile user expectations can be quantified even before any vibration is presented. Such expected perceptu-al specifications are the prerequisite for a subsequent translation into phys-ical vibration parameters. Plausibility can be understood as a similarity judgment between elicited features and expected features. Thus, plausible vibration can be synthesized by maximizing the similarity of the elicited perceptual properties to the expected perceptual properties. Based on the observed relationships between vibration parameters and sensory tactile perceptual attributes, a 1-nearest-neighbor model and a regression model were built. The plausibility of the vibrations synthesized by these models in the context of virtual auditory-visual-tactile vehicle scenes was validat-ed in a perceptual study. The results demonstrated that the perceptual spec-ifications obtained with the design language are sufficient to synthesize vibrations, which are perceived as equally plausible as recorded vibrations in a given situational context. Overall, the demonstrated design method can be a new, more efficient tool for designers authoring vibrations for virtual environments or creating tactile feedback. The method enables further automation of the design process and thus potential time and cost reductions.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 237Eines der zentralen Ziele des Designs von Produkten oder virtuellen Um-gebungen ist die Schaffung von Erfahrungen, die im beabsichtigten An-wendungskontext die Erwartungen der Benutzer erfüllen. Gegenwärtig versucht man im vibrotaktilen Authoring-Prozess mit ineffizienten Trial-and-Error-Verfahren, die Erwartungen an den dargestellten, virtuellen Situationskontext implizit zu erfüllen. Ein effizienteres, modellgetriebenes Verfahren, das explizit auf den taktilen Benutzererwartungen basiert, wäre daher von Vorteil. Im Alltag sind wir häufig verschiedenen Ganzkörper-schwingungen ausgesetzt. Abhängig von ihren zeitlichen und spektralen Eigenschaften assoziieren wir intuitiv bestimmte Wahrnehmungsmerkmale wie z.B. “kribbeln”. Dies legt eine systematische Beziehung zwischen physikalischen Parametern und Wahrnehmungsmerkmalen nahe. Um mit potentiellen Nutzern über hervorgerufene oder erwartete taktile Eigen-schaften zu kommunizieren, wird eine standardisierte Designsprache vor-geschlagen. Sie enthält eine Menge von sensorisch-taktilen Wahrneh-mungsmerkmalen, die hinreichend den Wahrnehmungsraum der im Alltag auftretenden Vibrationen charakterisieren. Diese Entwurfssprache ermög-licht die quantitative Beurteilung taktiler Wahrnehmungsmerkmale, die in Situationskontexten wie z.B. auditiv-visuell-taktilen Fahrzeugszenen her-vorgerufen werden. Solche Wahrnehmungsspezifikationen können jedoch auch bewertet werden, indem der Inhalt dieser Szenen verbal beschrieben wird. Quasi identische Bewertungen für beide Präsentationsmodi deuten darauf hin, dass die taktilen Benutzererwartungen quantifiziert werden können, noch bevor eine Vibration präsentiert wird. Die erwarteten Wahr-nehmungsspezifikationen sind die Voraussetzung für eine anschließende Übersetzung in physikalische Schwingungsparameter. Plausible Vibratio-nen können synthetisiert werden, indem die erwarteten Wahrnehmungs-merkmale hervorgerufen werden. Auf der Grundlage der beobachteten Beziehungen zwischen Schwingungs¬parametern und sensorisch-taktilen Wahrnehmungsmerkmalen wurden ein 1-Nearest-Neighbor-Modell und ein Regressionsmodell erstellt. Die Plausibilität der von diesen Modellen synthetisierten Schwingungen im Kontext virtueller, auditorisch-visuell-taktiler Fahrzeugszenen wurde in einer Wahrnehmungsstudie validiert. Die Ergebnisse zeigten, dass die mit der Designsprache gewonnenen Wahr-nehmungsspezifikationen ausreichen, um Schwingungen zu synthetisieren, die in einem gegebenen Situationskontext als ebenso plausibel empfunden werden wie aufgezeichnete Schwingungen. Die demonstrierte Entwurfsme-thode stellt ein neues, effizienteres Werkzeug für Designer dar, die Schwingungen für virtuelle Umgebungen erstellen oder taktiles Feedback für Produkte erzeugen.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 23

Experimental identification of the behaviour of and lateral forces from freely-walking pedestrians on laterally oscillating structures in a virtual reality environment

AbstractModelling pedestrian loading on lively structures such as bridges remains a challenge. This is because pedestrians have the capacity to interact with vibrating structures which can lead to amplification of the structural response. Current design guidelines are often inaccurate and limiting as they do not sufficiently acknowledge this effect. This originates in scarcity of data on pedestrian behaviour on vibrating ground and uncertainty as to the accuracy of results from previous experimental campaigns aiming to quantify pedestrian behaviour in this case. To this end, this paper presents a novel experimental setup developed to evaluate pedestrian actions on laterally oscillating ground in the laboratory environment while avoiding the implications of artificiality and allowing for unconstrained gait. A biologically-inspired approach was adopted in its development, relying on appreciation of operational complexities of biological systems, in particular their adaptability and control requirements. In determination of pedestrian forces to the structure consideration was given to signal processing issues which have been neglected in past studies. The results from tests conducted on the setup are related to results from previous experimental investigations and outputs of the inverted pendulum pedestrian model for walking on laterally oscillating ground, which is capable of generating self-excited forces

Mirror-image relations in category learning

The discrimination of patterns that are mirror-symmetric counterparts of each other is difficult and requires substantial training. We explored whether mirror-image discrimination during expertise acquisition is based on associative learning strategies or involves a representational shift towards configural pattern descriptions that permit resolution of symmetry relations. Subjects were trained to discriminate between sets of unfamiliar grey-level patterns in two conditions, which either required the separation of mirror images or not. Both groups were subsequently tested in a 4-class category-learning task employing the same set of stimuli. The results show that subjects who had successfully learned to discriminate between mirror-symmetric counterparts were distinctly faster in the categorization task, indicating a transfer of conceptual knowledge between the two tasks. Additional computer simulations suggest that the development of such symmetry concepts involves the construction of configural, protoholistic descriptions, in which positions of pattern parts are encoded relative to a spatial frame of reference

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes