Adaptive Tutoring on a Virtual Reality Driving Simulator

We propose a system for a VR driving simulator including an \ac{its} to train the user's driving skills. TheVR driving simulator comprises a detailed model of a city, VR traffic, and a physical driving engine, interacting with the driver. In a physical mockup of a car cockpit, the driver operates the vehicle through the virtual environment by controlling a steering wheel, pedals, and a gear lever. Using a HMD, the driver observes the scene from within the car. The realism of the simulation is enhanced by a 6 DOF motion platform, capable of simulating forces experienced when accelerating, braking, or turning the car. Based on a pre-defined list of driving-related activities, the ITS permanently assesses the quality of driving during the simulation and suggests an optimal path through the city to the driver in order to improve the driving skills. A user study revealed that most drivers experience presence in the virtual world and are proficient in operating the car

Hyper-reoriented walking in minimal space

We present a new reorientation technique, "hyper-reoriented walking," which greatly reduces the amount of physical space required in virtual reality (VR) applications asking participants to walk along a grid-like path (such as the most common layout in department stores). In hyper-reoriented walking, users walk along the gridlines with a virtual speed of twice the speed of real walking and perform turns at cross-points on the grid with half the speed of the rotation speed in the physical space. The impact of the technique on participants' sense of orientation and increase in simulator sickness was investigated experimentally involving 19 participants walking in a labyrinth of infinite size that included straight corridors and 90 degrees T-junctions at the end of the corridors. Walking accuracy was assessed by tracking the position of the head mounted display, and cyber-sickness was recorded with the simulator sickness questionnaire and with open questions. Walking straight forward was found to closely match the ideal path, which is the grid line, but slight errors occasionally occurred when participants turned at the T-junctions. A correction algorithm was therefore necessary to bring users back to the gridline. For VR experiments in a grid-like labyrinth with paths of 5 m in length, the technique reduces required size of the tracked physical walking area to 3 m x 2 m.ISSN:1434-9957ISSN:1359-433

Protecting the children —a virtual reality experiment on consumers’ risk perceptions of household chemicals

Warnings on the labels of hazardous household chemicals (e.g. warning pictograms and use instructions) should create risk awareness and thus encourage safe storage, handling and disposal. However, scientific findings have called into question the effectiveness of warnings to prevent accidents, albeit mostly based on consumers' self-reports. This study aimed to contribute to existing data on household chemicals and consumer safety by applying a novel observational method using virtual reality (VR). The study participants (N = 119) were observed after receiving a task to childproof a virtual apartment from various interactable neutral and hazardous objects (i.e. knives, lighters and household chemicals with and without warnings) that were placed in the virtual apartment. For the analysis, the object placement (i.e. accessible or inaccessible to the children) and the level of interaction with the objects were coded, and the observational data were supplemented with data gathered through a questionnaire. The results showed that most hazardous household chemicals were moved to spots that were inaccessible to the children without any interactions of the participants with the warnings. Instead, they used their pre-existing knowledge and intuitive strategies to judge the objects’ risks. These potentially misleading intuitive strategies should be increasingly considered in the regulation of hazardous household chemicals. This study also discussed the use and limitations of VR for the observation of human behaviour and decision making under uncertainty.ISSN:0003-687