I Am The Passenger: How Visual Motion Cues Can Influence Sickness For In-Car VR

This paper explores the use of VR Head Mounted Displays (HMDs) in-car and in-motion for the first time. Immersive HMDs are becoming everyday consumer items and, as they offer new possibilities for entertainment and productivity, people will want to use them during travel in, for example, autonomous cars. However, their use is confounded by motion sickness caused in-part by the restricted visual perception of motion conflicting with physically perceived vehicle motion (accelerations/rotations detected by the vestibular system). Whilst VR HMDs restrict visual perception of motion, they could also render it virtually, potentially alleviating sensory conflict. To study this problem, we conducted the first on-road and in motion study to systematically investigate the effects of various visual presentations of the real-world motion of a car on the sickness and immersion of VR HMD wearing passengers. We established new baselines for VR in-car motion sickness, and found that there is no one best presentation with respect to balancing sickness and immersion. Instead, user preferences suggest different solutions are required for differently susceptible users to provide usable VR in-car. This work provides formative insights for VR designers and an entry point for further research into enabling use of VR HMDs, and the rich experiences they offer, when travelling

How Visual Motion Cues Can Influence Sickness For In-Car VR

This video demonstrates our research into the use of VR Head Mounted Displays (HMDs) in-car and in-motion. Immersive HMDs offer new possibilities for entertainment and productivity during travel. However, their use is confounded by motion sickness, caused in-part by the conflict between visually and physically perceived motion. We examine how visual conveyance of motion affects motion sickness during in-car VR

Optimisation of a moving platform vehicle simulator for vehicle handling experiments

This thesis discusses the optimisation of motion platform simulators and was motivated by Loughborough University's acquisition of a low cost six strut moving platform vehicle simulator. Historically, we see that automotive vehicle simulators are more generally used for human factors experiments that examine driver behaviour during low severity manoeuvres or short events e.g. obstacle avoidance. The purpose of this thesis is to examine the potential for the simulator to be used for vehicle handling experiments where the vehicle is free to explore the limits of the vehicle for sustained periods of time. This research has a significant emphasis on vehicle handling models. In particular, we examine data acquisition systems and testing methods before investigating potential optimisation and identification techniques for estimating vehicle model parameters that have the potential to be implemented on the simulator. Here we examine the possibility of producing high quality vehicle models within a short space of time with a view to rapid identification of different types of vehicle directly from vehicle testing. This includes the data acquisition process and addresses the significance of the sensors and equipment used to measure the vehicle states and the importance of the recorded vehicle manoeuvres and test track characteristics. The second phase was carried out once the simulator was installed and functional. Clearly, the simulator is a piece of experimental equipment and as with any engineering experiment, the equipment should be well understood. Consequently, the accuracy to which it adheres to the real world, i.e. its fidelity, is assessed by investigating the simulators capabilities and limitations and is achieved by analysing the raw performance of the motion platform and conducting driver-in-the-Ioop experiments; this work proves valuable as it is used to optimise how the motion platform responds to vehicle dynamics and provides the motivation behind conducting a driver-in-the-Ioop handling experiment for the final section of this thesis. Here, the simulators potential to be used as a tool to assess race car driver skill is investigated. After conducting various tests in the simulated and real world, the correlation between the subjects simulated and real world performances are used to critically assess the simulators performance and draw conclusions concerning its future potential for handling based research. This thesis shows it possible to use an Inertial GPS Navigation System for capturing vehicle data to good effect and describes how a comprehensive set of new vehicle dynamics measurements can be collected and used for model tuning and optimisation within a relatively short space of time (approximately one day). The work presents substantial evidence that shows how dominant the influence of steer ratio and toe compliance is on the accuracy of the handling models and that they are a likely source of modelling errors. The importance of vehicle slip angle measurement is a particular point if of interest and is examined concurrently with the driving manoeuvres, where some guidelines for test methodology and data collection are established. A novel identification process is also presented with the Identifying Extended KaIman Filter. It has been shown possible to identify separate front and rear tyre models as well as a single tyre model. The thesis also describes the relative importance of motion for vehicle simulators that are to be used for handling based experiments. It appears more valuable to emulate only those vehicle motions that are within the platforms capabilities and limitations in a quest for quality over quantity. Finally, this work demonstrates the simulators potential to be used as tool to evaluate race car driver skill, which also fundamentally assesses the fidelity of the simulator. This is achieved by examining the correlation between a simulated and real world experiment, where we see a positive correlation which indicates high fidelity. Further analysis shows the importance that adequate driver training is being administered before beginning experimentation

Autonomous Control and Automotive Simulator Based Driver Training Methodologies for Vehicle Run-Off-Road and Recovery Events

Traffic fatalities and injuries continue to demand the attention of researchers and governments across the world as they remain significant factors in public health and safety. Enhanced legislature along with vehicle and roadway technology has helped to reduce the impact of traffic crashes in many scenarios. However, one specifically troublesome area of traffic safety, which persists, is run-off-road (ROR) where a vehicle\u27s wheels leave the paved portion of the roadway and begin traveling on the shoulder or side of the road. Large percentages of fatal and injury traffic crashes are attributable to ROR. One of the most critical reasons why ROR scenarios quickly evolve into serious crashes is poor driver performance. Drivers are unprepared to safely handle the situation and often execute dangerous maneuvers, such as overcorrection or sudden braking, which can lead to devastating results. Currently implemented ROR countermeasures such as roadway infrastructure modifications and vehicle safety systems have helped to mitigate some ROR events but remain limited in their approach. A complete solution must directly address the primary factor contributing to ROR crashes which is driver performance errors. Four vehicle safety control systems, based on sliding control, linear quadratic, state flow, and classical theories, were developed to autonomously recover a vehicle from ROR without driver intervention. The vehicle response was simulated for each controller under a variety of common road departure and return scenarios. The results showed that the linear quadratic and sliding control methodologies outperformed the other controllers in terms of overall stability. However, the linear quadratic controller was the only design to safely recover the vehicle in all of the simulation conditions examined. On average, it performed the recovery almost 50 percent faster and with 40 percent less lateral error than the sliding controller at the expense of higher yaw rates. The performance of the linear quadratic and sliding algorithms was investigated further to include more complex vehicle modeling, state estimation techniques, and sensor measurement noise. The two controllers were simulated amongst a variety of ROR conditions where typical driver performance was inadequate to safely operate the vehicle. The sliding controller recovered the fastest within the nominal conditions but exhibited large variability in performance amongst the more extreme ROR scenarios. Despite some small sacrifice in lateral error and yaw rate, the linear quadratic controller demonstrated a higher level of consistency and stability amongst the various conditions examined. Overall, the linear quadratic controller recovered the vehicle 25 percent faster than the sliding controller while using 70 percent less steering, which combined with its robust performance, indicates its high potential as an autonomous ROR countermeasure. The present status of autonomous vehicle control research for ROR remains premature for commercial implementation; in the meantime, another countermeasure which directly addresses driver performance is driver education and training. An automotive simulator based ROR training program was developed to instruct drivers on how to perform a safe and effective recovery from ROR. A pilot study, involving seventeen human subject participants, was conducted to evaluate the effectiveness of the training program and whether the participants\u27 ROR recovery skills increased following the training. Based on specific evaluation criteria and a developed scoring system, it was shown that drivers did learn from the training program and were able to better utilize proper recovery methods. The pilot study also revealed that drivers improved their recovery scores by an average of 78 percent. Building on the success observed in the pilot study, a second human subject study was used to validate the simulator as an effective tool for replicating the ROR experience with the additional benefit of receiving insight into driver reactions to ROR. Analysis of variance results of subjective questionnaire data and objective performance evaluation parameters showed strong correlations to ROR crash data and previous ROR study conclusions. In particular, higher vehicle velocities, curved roads, and higher friction coefficient differences between the road and shoulder all negatively impacted drivers\u27 recoveries from ROR. The only non-significant impact found was that of the roadway edge, indicating a possible limitation of the simulator system with respect to that particular environment variable. The validation study provides a foundation for further evaluation and development of a simulator based ROR recovery training program to help equip drivers with the skills to safely recognize and recover from this dangerous and often deadly scenario. Finally, building on the findings of the pilot study and validation study, a total of 75 individuals participated in a pre-post experiment to examine the effect of a training video on improving driver performance during a set of simulated ROR scenarios (e.g., on a high speed highway, a horizontal curve, and a residential rural road). In each scenario, the vehicle was unexpectedly forced into an ROR scenario for which the drivers were instructed to recover as safely as possible. The treatment group then watched a custom ROR training video while the control group viewed a placebo video. The participants then drove the same simulated ROR scenarios. The results suggest that the training video had a significant positive effect on drivers\u27 steering response on all three roadway conditions as well as improvements in vehicle stability, subjectively rated demand on the driver, and self-evaluated performance in the highway scenario. Under the highway conditions, 84 percent of the treatment group and 52 percent of the control group recovered from the ROR events. In total, the treatment group recovered from the ROR events 58 percent of the time while the control group recovered 45 percent of the time. The results of this study suggest that even a short video about recovering from ROR events can significantly influence a driver\u27s ability to recover. It is possible that additional training may have further benefits in recovering from ROR events

Modular Human-in-the-loop Design Framework Based on Human Factors

Human-in-the-loop design framework introduced in this dissertation utilizes Digital Human Modeling (DHM) to incorporate Human Factors Engineering (HFE) design principles early in design process. It embodies scientific methods (e.g., mathematics) and artistic approaches (e.g., visualization) to assess human well-being and overall system performance. This framework focuses not only on ergonomics assessments but also actual design process including, but not limited to, concept development, structural integrity and digital prototyping. It addresses to three major limitations found in HFE literature and practices

Stereoscopic 3D user interfaces : exploring the potentials and risks of 3D displays in cars

During recent years, rapid advancements in stereoscopic digital display technology has led to acceptance of high-quality 3D in the entertainment sector and even created enthusiasm towards the technology. The advent of autostereoscopic displays (i.e., glasses-free 3D) allows for introducing 3D technology into other application domains, including but not limited to mobile devices, public displays, and automotive user interfaces - the latter of which is at the focus of this work. Prior research demonstrates that 3D improves the visualization of complex structures and augments virtual environments. We envision its use to enhance the in-car user interface by structuring the presented information via depth. Thus, content that requires attention can be shown close to the user and distances, for example to other traffic participants, gain a direct mapping in 3D space

Advances in Mechanical Systems Dynamics 2020

The fundamentals of mechanical system dynamics were established before the beginning of the industrial era. The 18th century was a very important time for science and was characterized by the development of classical mechanics. This development progressed in the 19th century, and new, important applications related to industrialization were found and studied. The development of computers in the 20th century revolutionized mechanical system dynamics owing to the development of numerical simulation. We are now in the presence of the fourth industrial revolution. Mechanical systems are increasingly integrated with electrical, fluidic, and electronic systems, and the industrial environment has become characterized by the cyber-physical systems of industry 4.0. Within this framework, the status-of-the-art has become represented by integrated mechanical systems and supported by accurate dynamic models able to predict their dynamic behavior. Therefore, mechanical systems dynamics will play a central role in forthcoming years. This Special Issue aims to disseminate the latest research findings and ideas in the field of mechanical systems dynamics, with particular emphasis on novel trends and applications