Virtual Techniques for Prototype HMI Evaluation

The aim of this project was to investigate the behavioural validity of virtual methods, namely driving simulators and computational models, as prototype HMI evaluation tools. A driving study was designed where participants had to perform secondary tasks while driving in a real world and a driving simulator setting. Statistical analysis of the data, along with an in-depth review of related findings was used to identify the levels of behavioural validity that could be achieved by different simulator settings across different metrics. A further analysis was performed to identify behavioural strategies that drivers employ regarding their visual attention sharing while executing HMI tasks concurrently to driving. Finally, two existing computational models were validated and a novel model was proposed that can account for drivers’ behavioural phenomena, not previously accounted for

Identifying cognitive distraction using steering wheel reversal rates

The influence of driver distraction on driving performance is not yet well understood, but it can have detrimental effects on road safety. In this study, we examined the effects of visual and non-visual distractions during driving, using a high-fidelity driving simulator. The visual task was presented either at an offset angle on an in-vehicle screen, or on the back of a moving lead vehicle. Similar to results from previous studies in this area, non-visual (cognitive) distraction resulted in improved lane keeping performance and increased gaze concentration towards the centre of the road, compared to baseline driving, and further examination of the steering control metrics indicated an increase in steering wheel reversal rates, steering wheel acceleration, and steering entropy. We show, for the first time, that when the visual task is presented centrally, drivers’ lane deviation reduces (similar to non-visual distraction), whilst measures of steering control, overall, indicated more steering activity, compared to baseline. When using a visual task that required the diversion of gaze to an in-vehicle display, but without a manual element, lane keeping performance was similar to baseline driving. Steering wheel reversal rates were found to adequately tease apart the effects of non-visual distraction (increase of 0.5 degree reversals) and visual distraction with offset gaze direction (increase of 2.5 degree reversals). These findings are discussed in terms of steering control during different types of in-vehicle distraction, and the possible role of manual interference by distracting secondary tasks

PRAKSAGORAS: A group decision support system for the improvement of the health care services

Summarization: One of the main characteristics of the health sector is that consumes a large proportion of GDP across the European Union. For this reason in our days the health care policy makers use advanced models in order to evaluate the health care services and more over to redesign them in order to improve their effectiveness. Studying the academic literature it’s obvious that the majority of the models that have been developed for the improvement of the health care services don’t take into account the opinions of the citizens. Another limitation is that the majority of the methodologies take into account only the preference model of one decision maker. Some recent research approaches in order to tackle the aforementioned problem use satisfaction questionnaires in order to enable the citizens in the area of reorganization of the health care services. Praksagoras is a Group Decision Support System that has been developed in order to redesign the health care services and moreover to improve the decision making process by enabling many decision makers like CEO of the hospital, the Physicians and the patients. Praksagoras combines simulations, Multicriteria satisfaction analysis, social choice theory and the aggregation disaggregation approach of Multi-criteria analysis. More specifically, via the simulation model the system depicts the current operation of the emergency departments. The modeler has the ability to test alternatives scenarios for the emergency department in order to test the impacts of these alternatives scenarios on consistent set of criteria such as waiting times, length of stay, working load of the personnel and bed usage. The MUSA model reveals the weak points of the services giving the opportunity to the analyst to develop alternatives that are based on patients’ needs. In order to enable many stakeholders (doctors, patients etc.) in the decision making process we use the RACES approach. Races approach incorporates the social choice functions for aggregating individual rankings with MINORA and MIIDAS systems, in order to assess value function(s), as compatible as possible with a collective ranking. The main aim of this approach is to provide mechanisms for the support of the analysis of the individual and collective preference models and to enrich the knowledge of the decision problem and the structure of DM's preferences.Παρουσιάστηκε στο: 26th National Conference on Operational Researc