Motion cueing in driving simulators for research applications

This research investigated the perception of self-motion in driving simulation, focussing on the dynamic cues produced by a motion platform. The study was undertaken in three stages, evaluating various motion cueing techniques based on both subjective ratings of realism and objective measures of driver performance. Using a Just Noticeable Difference methodology, Stage 1 determined the maximum perceptible motion scaling for platform movement in both translation and tilt. Motion cues scaled by 90% or more could not be perceptibly differentiated from unscaled motion. This result was used in Stage 2‟s examination of the most appropriate point in space at which the platform translations and rotations should be centred (Motion Reference Point, MRP). Participants undertook two tracking tasks requiring both longitudinal (braking) and lateral (steering) vehicle control. Whilst drivers appeared unable to perceive a change in MRP from head level to a point 1.1m lower, the higher position (closer to the vestibular organs) did result in marginally smoother braking, corresponding to the given requirements of the longitudinal driving task. Stage 3 explored the perceptual trade-off between the specific force error and tilt rate error generated by the platform. Three independent experimental factors were manipulated: motion scale-factor, platform tilt rate and additional platform displacement afforded by a XY-table. For the longitudinal task, slow tilt that remained sub-threshold was perceived as the most realistic, especially when supplemented by the extra surge of the XY-table. However, braking task performance was superior when a more rapid tilt was experienced. For the lateral task, perceived realism was enhanced when motion cues were scaled by 50%, particularly with added XY-sway. This preference was also supported by improvements in task accuracy. Participants ratings were unmoved by changing tilt rate, although rapid tilt did result in more precise lane control. Several interactions were also observed, most notably between platform tilt rate and XY-table availability. When the XY-table was operational, driving task performance varied little between sub-threshold and more rapid tilt. However, while the XY-table was inactive, both driving tasks were better achieved in conditions of high tilt rate. An interpretation of these results suggests that without the benefit of significant extra translational capability, priority should be given to the minimisation of specific force error through motion cues presented at a perceptibly high tilt rate. However, XY-table availability affords the simulator engineer the luxury of attaining a slower tilt that provides both accurate driving task performance and accomplishes maximum perceived realism

Model-Based Control Techniques for Automotive Applications

Two different topics are covered in the thesis. Model Predictive Control applied to the Motion Cueing Problem In the last years the interest about dynamic driving simulators is increasing and new commercial solutions are arising. Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices: in fact, on the one hand, having a human driver on a driving simulator allows automotive manufacturers to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase; on the other hand, novel driver assistance systems (such as advanced accident avoidance systems) can be safely tested by having the driver operating the vehicle in a virtual, highly realistic environment, while being exposed to hazardous situations. In both applications, it is crucial to faithfully reproduce in the simulator the driver's perception of forces acting on the vehicle and its acceleration. This has to be achieved while keeping the platform within its limited operation space. Such strategies go under the name of Motion Cueing Algorithms. In this work, a particular implementation of a Motion Cueing algorithm is described, that is based on Model Predictive Control technique. A distinctive feature of such approach is that it exploits a detailed model of the human vestibular system, and consequently differs from standard Motion Cueing strategies based on Washout Filters: such feature allows for better implementation of tilt coordination and more efficient handling of the platform limits. The algorithm has been evaluated in practice on a small-size, innovative platform, by performing tests with professional drivers. Results show that the MPC-based motion cueing algorithm allows to effectively handle the platform working area, to limit the presence of those platform movements that are typically associated with driver motion sickness, and to devise simple and intuitive tuning procedures. Moreover, the availability of an effective virtual driver allows the development of effective predictive strategies, and first simulation results are reported in the thesis. Control Techniques for a Hybrid Sport Motorcycle Reduction of the environmental impact of transportation systems is a world wide priority. Hybrid propulsion vehicles have proved to have a strong potential to this regard, and different four-wheels solutions have spread out in the market. Differently from cars, and even if they are considered the ideal solution for urban mobility, motorbikes and mopeds have not seen a wide application of hybrid propulsion yet, mostly due to the more strict constraints on available space and driving feeling. In the thesis, the problem of providing a commercial 125cc motorbike with a hybrid propulsion system is considered, by adding an electric engine to its standard internal combustion engine. The aim for the prototype is to use the electrical machine (directly keyed on the drive shaft) to obtain a torque boost during accelerations, improving and regularizing the supplied power while reducing the emissions. Two different control algorithms are proposed 1) the first is based on a standard heuristic with adaptive features, simpler to implement on the ECU for the prototype; 2) the second is a torque-split optimal-control strategy, managing the different contributions from the two engines. A crucial point is the implementation of a Simulink virtual environment, realized starting from a commercial tool, VI-BikeRealTime, to test the algorithms. The hybrid engine model has been implemented in the tool from scratch, as well as a simple battery model, derived directly from data-sheet characteristics by using polynomial interpolation. The simulation system is completed by a virtual rider and a tool for build test circuits. Results of the simulations on a realistic track are included, to evaluate the different performance of the two strategies in a closed loop environment (thanks to the virtual rider). The results from on-track tests of the real prototype, using the first control strategy, are reported too

A Case Study on Vestibular Sensations in Driving Simulators

Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will not be satisfactory. This paper shows a case study where a BMW 325Xi AUT fitted with a sensor, recorded the accelerations produced in all degrees of freedom (DOF) during several runs, and data have been introduced in mathematical simulation software (washout + kinematics + actuator simulation) of a 6DOF motion platform. The input to the system has been qualitatively compared with the output, observing that most of the simulation adequately reflects the input to the system. Still, there are three events where the accelerations are lost. These events are considered by experts to be of vital importance for the outcome of a learning process in the simulator to be adequat

Improved Multispectral Skin Detection and its Application to Search Space Reduction for Dismount Detection Based on Histograms of Oriented Gradients

Due to the general shift from conventional warfare to terrorism and urban warfare by enemies of the United States in the late 20th Century, locating and tracking individuals of interest have become critically important. Dismount detection and tracking are vital to provide security and intelligence in both combat and homeland defense scenarios including base defense, combat search and rescue (CSAR), and border patrol. This thesis focuses on exploiting recent advances in skin detection research to reliably detect dismounts in a scene. To this end, a signal-plus-noise model is developed to map modeled skin spectra to the imaging response of an arbitrary sensor, enabling an in-depth exploration of multispectral features as they are encountered in the real world for improved skin detection. Knowledge of skin locations within an image is exploited to cue a robust dismount detection algorithm, significantly improving dismount detection performance and efficiency. This research explores multiple spectral features and detection algorithms to find the best features and algorithms for detecting skin in multispectral visible and short wave infrared (SWIR) imagery. This study concludes that using SWIR imagery for skin detection and color information for false alarm suppression results in 95% probability of skin detection at a false alarm rate of only 0.4%. Skin detections are utilized to cue a dismount detector based on histograms of oriented gradients. This technique reduces the search space by nearly 3 orders of magnitude compared to searching an entire image, while reducing the average number of false positives per image by nearly 2 orders of magnitude at 95% probability of dismount detection. The skin-detection-cued dismount detector developed in this thesis has the potential to make significant contribution to the United States Air Force human measurement and signature intelligence and CSAR missions

Eine Studie über State-of-the-Art Motion-Cueing-Algorithmen angewendet auf Planar Motion mit Pure Lateral Acceleration - Vergleich, Auto-Tuning und subjektive Bewertung auf einem KUKA Robocoaster Serial Ride Simulator

Driving simulators are widely used during the training of drivers/pilots, and for entertainment, as well as in the research of human behavior. Motion cueing algorithms (MCAs) are aimed at mapping the motion of moving vehicles into the limited workspace of driving simulators, while preserving the perceptual realism of the simulation. Several MCAs have been developed in the literature to improve the realism of the simulation. However, each MCA presents strengths and weaknesses when compared to the others. Most importantly, the tuning of the MCA parameters is an open issue because these parameters are not intuitive for normal simulator users. The current dissertation considers a systematic comparison of existing MCAs based on a simple maneuver with only lateral (left/right) accelerations, i.e. a trajectory along a planar S-shaped track with constant velocity. All the MCAs were implemented and compared numerically using a novel measure – the well-tuned index. In a later stage, the trajectories of the MCAs were implemented in the KUKA Robocoaster and assessed by a group of 17 test-subjects. The results of the subjective assessment were compared with the numerical metrics. Furthermore, in the thesis an auto-tuning process based on the mean-variance mapping optimization (MVMO) and numerical perception scores was developed by which one is able to automatically tune the parameters to obtain a high well-tuned index, reducing drastically the current high manual tuning times. It was observed that, for a serial robot, the circular motion of the cabin can be well-compensated by the pitch angle. Among the MCAs, the optimal tracking algorithm (ZyRo) and the model predictive control (MPC*) can simulate large amplitude input signals while keeping a high value of the well-tuned index. Furthermore, these algorithms exploit the simulator’s workspace better than the other MCAs and are easily tuned. The ZyRo algorithm produces similar results as the MPC* algorithm, but requires less computational time. The responses of all the MCAs included in this study are similar when using a same low scaling factor (k = 0.4). However, if a larger scale factor is used, the responses of the MCAs change significantly. Finally, it was concluded that a good correlation between the average subjective scores and the objective measures can be achieved. However, due to the large variability of the individual scores, further research is needed to better understand the subjective ratings of simulation rides.Fahrsimulatoren werden für die Schulung von Fahrern/Piloten, in Fahrgeschäften, sowie in der Forschung des menschlichen Verhaltens verwendet. Motion-Cueing-Algorithmen (MCAs) zielen darauf ab, die Bewegung von fahrenden Fahrzeugen in dem begrenzten Arbeitsbereich der Fahrsimulatoren abzubilden und gleichzeitig den Wahrnehmungsrealismus der Simulation zu bewahren. In der Literatur wurden mehrere MCAs entwickelt, um den Realismus der Simulation zu verbessern. Jedoch weist jeder MCA Stärken und Schwächen im Vergleich zu den anderen. Dabei ist die Abstimmung der MCA-Parameter kritisch, da die Parameter für normale Simulator-Benutzer nicht intuitiv sind. In dieser Dissertation wird ein systematischer Vergleich zwischen bestehenden MCAs anhand eines einfachen Manövers mit nur seitlichen (links / rechts) Beschleunigungen, d.h. entlang einer ebenen S-förmigen Spur mit konstanter Geschwindigkeit durchgeführt. Alle MCAs wurden implementiert und numerisch mit einem neuartigen Maß - dem „gut abgestimmten Index“ („well-tuned index“) – miteinander verglichen. In einem späteren Stadium wurden die Trajektorien der MCAs im KUKA Robocoaster implementiert und von einer Gruppe von 17 Versuchspersonen bewertet. Die Ergebnisse der subjektiven Bewertung wurden dargestellt und mit den numerischen Metriken verglichen. In der Dissertation wird auch eine neue Auto-Tuning Methode entwickelt, die die Mittel-Varianz-Mapping-Optimierung (Mean Variance Mapping Optimization = MVMO) sowie numerische Empfindungscores verwendet, mit der die Parameter automatisch abgestimmt und damit hohe Werte des Bewegungsempfindungsindex erzielt werden können. Dadurch entfallen die sehr zeitaufwändigen manuellen Abstimmungsarbeiten bei gleicher oder besserer Bewegungsqualität. Es wurde beobachtet, dass für einen seriellen Roboter die Kreisbewegung der Kabine durch den Nickwinkel gut kompensiert werden kann. Unter den MCAs konnten der optimale Tracking-Algorithmus (ZyRo) und die modellbasierte prädiktive Regelung (MPC*) große Amplituden des Eingangssignals unter Erzielung eines hohen Wert des „gut abgestimmten Index“ simulieren. Darüber hinaus nutzten diese Algorithmen den Arbeitsbereich des Simulators besser als die anderen MCAs und waren leichter abzustimmen. Der ZyRo-Algorithmus erzeugt ähnliche Ergebnisse wie der MPC*-Algorithmus, erfordert aber weniger Rechenzeit. Die Bewegungseigenschaften aller in dieser Studie enthaltenen MCAs sind bei der Verwendung des gleichen niedrigen Skalierungsfaktors (k = 0,4) ähnlich. Wenn jedoch ein größerer Skalierungsfaktor verwendet wird, ändern sich die Antworten der MCAs signifikant. Zum Schluss wurde festgestellt, dass eine gute Korrelation zwischen den durchschnittlichen subjektiven Bewertungen und den objektiven Maßnahmen erreicht werden kann. Allerdings sind aufgrund der großen Variabilität der einzelnen Bewertungen weitere Untersuchungen erforderlich, um die subjektiven Bewertungen der Fahrsimulationen besser zu verstehen

Enhancing human motion perception in model predictive motion cueing algorithm

In this research, the predictive motion cueing algorithm has been optimized for improving a human driver sensation based on the mathematical model. The Model Predictive Control cost function and the prediction and control horizons are optimized. The future trajectory is predicted by artificial intelligence and the related control actions are applied beforehand in real-time

A Survey of Driving Research Simulators Around the World.

The literature review is part of the EPSRC funded project "Driver performance in the EPSRC driving simulator: a validation study". The aim of the project is to validate this simulator, located at the Department of Psychology, University of Leeds, and thereby to indicate the strengths and weaknesses of the existing configuration. It will provide guidance on how the simulator can be modified and overcome any deficiencies that are detected and also provide "benchmarks" against which other simulators can be compared. The literature review will describe the technical characteristics of the most well-known driving simulators around the world, their special features and their application areas until today. The simulators will be described and compared according to their cost (low, medium and high) and also contact addresses and photographs of the simulators will be provided by the end of the paper. In the process of gathering this information, it became apparent that there are mainly two types of papers published - either in journals or in proceedings from conferences: those describing only the technical characteristics of a specific simulator and those referring only to the applications of a specific simulator. For the first type of papers, the level of detail, format and content varies significantly where for the second one it has been proven extremely difficult to find any information about the technical characteristics of the simulator where the study had been carried out. A number of details provided in this paper are part of personal communication, or personal visits to those particular driving simulator centres or from the World Wide Web. It should also be noted here that most of the researchers contacted here offered very detail technical characteristics and application areas of their driving simulators and the author is grateful to them

The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations

Activity Based Intelligence (ABI) is the derivation of information from a series of in- dividual actions, interactions, and transactions being recorded over a period of time. This usually occurs in Motion imagery and/or Full Motion Video. Due to the growth of unmanned aerial systems technology and the preponderance of mobile video devices, more interest has developed in analyzing people\u27s actions and interactions in these video streams. Currently only visually subjective quality metrics exist for determining the utility of these data in detecting specific activities. One common misconception is that ABI boils down to a simple resolution problem; more pixels and higher frame rates are better. Increasing resolution simply provides more data, not necessary more informa- tion. As part of this research, an experiment was designed and performed to address this assumption. Nine sensors consisting of four modalities were place on top of the Chester F. Carlson Center for Imaging Science in order to record a group of participants executing a scripted set of activities. The multimodal characteristics include data from the visible, long-wave infrared, multispectral, and polarimetric regimes. The activities the participants were scripted to cover a wide range of spatial and temporal interactions (i.e. walking, jogging, and a group sporting event). As with any large data acquisition, only a subset of this data was analyzed for this research. Specifically, a walking object exchange scenario and simulated RPG. In order to analyze this data, several steps of preparation occurred. The data were spatially and temporally registered; the individual modalities were fused; a tracking algorithm was implemented, and an activity detection algorithm was applied. To develop a performance assessment for these activities a series of spatial and temporal degradations were performed. Upon completion of this work, the ground truth ABI dataset will be released to the community for further analysis

Dual Loop Rider Control of a Dynamic Motorcycle Riding Simulator

Compared to the automotive industry, the use of simulators in the motorcycle domain is negligible as for their lack of usability and accessibility. According to the state-of-the-art, it is e.g. not possible for motorcyclists to intuitively control a high-fidelity dynamic motorcycle riding simulator when getting in contact with it for the first time. There are four main reasons for the insufficient simulation quality of dynamic motorcycle riding simulators: ▪ The instability of single-track vehicles at low speed, ▪ The steering force-feedback with highly velocity-dependent behavior, ▪ Motion-simulation (high dynamics, roll angle, direct contact to the environment), ▪ The specific influence of the rider to vehicle dynamics (incl. rider motion). The last bullet point is peculiar for motorcycles and dynamic motorcycle riding simulators in comparison with other vehicle simulators, as motorcycles are significantly affected in their dynamics by the rider’s body motion. However, up until today, almost no special emphasis has been put on the consideration of rider motion on dynamic motorcycle riding simulators. In this thesis, a motorcycle riding simulator is designed, constructed and put into operation. The focus here is attaching a real rider to a virtual motorcycle. Based on a commercially available multi-body-simulation model, a simulator architecture is designed, that allows to control the virtual motorcycle not only by steering, but by rider leaning as well. This is realized by determining the so-called rider induced roll torque, that allows a holistic measurement of the apparent coupling forces between rider and simulator mockup. Performance measures and study concepts are developed that allow to rate the system. In expert and participant studies, the influence of the system on the riding behavior of the simulator is investigated. It is shown that the rider motion determination allows realistic control inputs and has a positive effect on the stabilization at various velocities. The feedback of the rider induced roll torque to the virtual dynamics model allows study participants to control the virtual motorcycle more intuitively. The vehicle states during cornering are affected as expected from real riding. First results indicate that it becomes easier for naïve study participants to access the simulator in first-contact scenarios. The achieved improvements regarding the rideability of the simulator however do not suffice to overcome the abovementioned challenges to a degree that allows for a completely intuitive interaction with the simulator throughout the whole dynamic range

Development and application of smart actuation methods for vehicle simulators

Driving simulators are complex virtual reality systems that integrate visual displays, sound rendering systems, motion platforms, and human-machine-interface devices. They are used in different research areas and consequently, different studies are conducted using these systems, in conditions that would not be safe to be carried out in the real world. However, driving simulators are very expensive research tools. When building such a system, a compromise usually has to be made. Although a driving simulator cannot reproduce 1:1 real life situations or sensations because of its limitations, researchers still need to use such a device for training and research purposes, due to the realistic driving experience it has to offer to its driver. This work focuses on developing a three-degrees of freedom Essential Function Driving Simulator that integrates cost and design constraints, the human perception of motion and real vehicle motion achieved through simulated vehicle models, and the classical motion cueing strategy. The goal is, on the first hand, to immerse the driver to a certain extend into the simulation environment by using this virtual reality device and, on the second hand, to investigate the degree of realism of such a solution. Different actuation solutions are modelled and discussed in this research, with respect to the available workspace, singularity configurations, the system’s behaviour and the maximum forces needed in the frame of the overall cost constraints. A solution was chosen following kinematical and dynamical analyses, as a trade off solution among the above mentioned constraints. The human body finds itself in continuous movement and interaction with the environment. Motion is sensed by the human being through the vestibular system and the skin. The human motion perception mechanisms are mathematically modelled and studied, in order to apply their characteristics in the three-degrees of freedom driving simulator. Due to the limited workspace and degrees of freedom of the discussed simulator, the motion of the simulated vehicle cannot be identically reproduced by the motion system. Thus, special algorithms are designed to transform the motion of the vehicle model in achievable positions for the three actuators, and additionally, to render correct motion cues. The influence of the three variable parameters on the overall subjective degree of freedom is investigated using an optimisation method. The studied parameters are: motion, optical flow and haptic response, introduced by using a lane departure warning assistance system. It is shown in this research that the influence of motion cues on the subjective degree of realism rated by the drivers is of 84%. The vibrations in the steering wheel improve the realism of the simulation and have a 15% impact. The participants of these experiments could easily adapt to the provided assistance system and their immersion in the simulated environment was significantly influenced by the activation of the lane departure warning option. It has also been shown that drivers rated the motion and the accelerations felt in the simulator with 70.41%, compared to the experience of driving a real vehicle. These results are interpreted in this research by putting the emphasis on the fact that irrespective of the DOF of the actuation mechanism, a motion driving simulator should provide correct motion cues. The development of the vehicle models and of the motion cueing algorithms should be approached, so that the system provides motion as similar as possible to the real vehicle, as it is further discussed here.Entwicklung und Anwendung von intelligenten Ansteuerungsmethoden für Fahrzeugsimulatoren Fahrsimulatoren sind Virtual-Reality Systeme, die aus geeigneten Mensch-Maschinen-Schnittstellen, optische und akustische Wiedergabe und, wenn das Bedarf besteht, aus einen Bewegungsapparat bestehen. Sie werden in unterschiedlichen Forschungsfeldern verwendet um verschiedene Studien durchzuführen. Unter anderem können dadurch Manöverstudien durchgeführt werden, die in realen Fahrsituationen zu gefährlich für den Fahrer wären. Der Bau komplexer und hochauflösender Fahrsimulationen ist jedoch sehr Kostenintensiv. Obwohl ein Fahrsimulator die in realen Fahrsituationen empfundenen Fahrgefühle nicht originalgetreu wiedergegeben kann, durch den begrenzten Arbeitsraum, eignet sich ein solches Gerät zu Lehr- und Forschungszwecken. Diese Arbeit befasst sich mit der Entwickelung eines kostengünstigen Fahrsimulators mit drei Freiheitsgraden, der durch eine geeignete Motion-Cueing Strategie dem Fahrer ein ausreichendes Fahrgefühl widergibt. Es werden verschiedene Aktuierungslösungen in Bezug auf den begrenzten Arbeitsraum, singulären Stellungen, des maximalen Kraftbedarfs modelliert, verglichen und diskutiert. Es wurde eine Kompromisslösung gefunden basierend auf der kinematischen und dynamischen Analyse, die diese Begrenzungen berücksichtigt. Der menschliche Körper befindet sich in einer kontinuierlichen Bewegung und interagiert dabei mit der Umgebung. Die Bewegung wird durch das Vestibularorgan und durch die Haut wahrgenommen. Die menschliche Wahrnehmung wird durch ein geeignetes mathematisches Modell widergegeben. Der Bewegungsablauf des Fahrsimulators wurde unter Berücksichtigung der menschlichen Wahrnehmung ausgelegt und untersucht. Wegen dem begrenztem Arbeitsraum und der geringen Anzahl von Systemfreiheitsgraden kann der Simulator die reelle Fahrdynamik nicht im vollen Umfang an die Testperson weitergeben. Deshalb werden angepasste Algorithmen entwickelt um den Bewegungsablauf beschränkt durch drei Aktuatoren in einem akzeptablem umfang widerzugeben. Der Einfluss der drei Aktuatorparameter auf den Bewegungsablauf wird durch geeignete Optimierungsmethoden untersucht. Die Größen die anschließend durch das Fahrsimulator Setup untersucht werden sind unter anderem der Bewegungsablauf, die optische Darstellung und die haptische Wiedergabe. Die Wichtigkeit der empfundenen Fahrbewegung wurde durch die Probanden, im Vergleich zu einem statischen Fahrsimulator, mit 84% bewertet. Die Vibrationen im Lenkrad erhöhen das Realitätsempfinden um 15%. Die Testpersonen konnten sich schnell an die aktuierten Fahrsimulation anpassen und auch Assistenzsysteme wie Spurhalteassistent benutzen. Es wurde gezeigt, dass die im Fahrsimulator gefühlten Beschleunigungen zu ca. 70% an die im realen Fahrbetrieb empfundenen Beschleunigungen herankommen. Es hat sich gezeigt, dass der Immersionsgrad vor allem vom verwendeten Fahrzeugmodellen und den Motion-Cueing Algorithmus abhängig ist