Training and evaluation in a large-scale virtual environment for a location-based mobile application

Virtual reality (VR) training has been applied to multiple different tasks and compared to conventional training methods. The aim of this research is to evaluate training in a large-scale CAVE-based virtual environment (VE) for a location-based mobile application designed to support an address verification task via walking. This training required a simulated neighborhood environment, a user interface for VE navigation, and communication between the VE and the mobile application. In this research, four main elements interacted together to provide the user experience: the VE, the mobile application, the task, and the user’s spatial ability. The first part of the research examined the impact of training in VR. The training was applied in two different environments with two different groups (in the field and VR). The effectiveness of training was measured using a field test for both training groups. There were statistically significant improvements in both training groups after training. There were no significant differences between the two training groups in performance (time, distance, task errors) in the testing session. The second part evaluated the interaction with the mobile application by assessing the impact of training on the use of the mobile application and the usability issues experienced with the app. Usability was measured using quantitative (taps in the application) and qualitative (usability questionnaire) methods. Results showed statistically significant improvements in app interaction in both real-world and virtual training groups where both groups reduced taps significantly in the testing session compared to the training session. There were no significant differences between the two training groups in taps in the testing session. The usability questionnaire documented issues related to feedback and map design. However, the questionnaire results showed an overall satisfaction of the usefulness and the information quality of the mobile app. The last part of the study evaluated the interaction with the virtual environment by examining users’ sense of presence and perceived distance traveled. The sense of presence was measured using a presence questionnaire. The perceived distance compared participants’ perceived travel distance with their actual distance traveled. Results showed that participants had a relatively high sense of presence in the CAVE. It was also found that participants underperceived their distance traveled in VR. This research found the large-scale CAVE-based virtual environment (VE) valuable in training and evaluation for the location-based mobile application designed to support an address verification task. These results will enable users from both academia and industry with location-based mobile applications to apply sufficient training in the context in the large-scale CAVE-based virtual environment (VE). Findings in this research enable researchers and practitioners in user experience (UX) to apply valuable evaluations of usability and interaction for location-based mobile applications using a large-scale CAVE-based virtual environment (VE)

Turn Detection and Analysis of Turn Parameters for Driver Characterization

Advanced Driver Assistance Systems, or ADAS, which can notify the driver of potential dangers or even perform emergency maneuvers in dangerous situations, have been shown to play a crucial role in accident prevention and driver feedback. An Intelligent ADAS, or i-ADAS, relies on information about the state of the driver, their behavior or condition, the vehicle and the environment. Understanding the behavior requires the development of driver models, which can help predict how a person may react in certain situations or help determine if the individual is not performing at their usual level of ability. A key element in building such models is the ability to detect and analyze common driving maneuvers, such as making turns, on an individual-by-individual basis. Thus algorithms are needed which can detect and characterize individual driving maneuvers. In this research, we present a position-based turn detection algorithm for detecting turns from vehicle data and GPS coordinates. Based on a dataset of sixteen drivers involving 278 turns, the algorithm achieves an accuracy of 97.84%. The turn parameters detected by the algorithm are then averaged for each driver and clustered using K-Means. Turn parameters t - 5 seconds are also clustered prior to each detected turn and t + 5 seconds are clustered after each turn. The cluster centroids at each point in time determine particular driving behaviours which are summarized in four categories, and the cluster assignments are examined over time to categorize drivers into these behaviour categories. This analysis reveals two optimal times for analyzing driver behaviour. Our overall aim is to be able to build automated methods that can use this research to eventually determine characteristics of individual drivers during turns in order to build models of drivers for use with i-ADAS