Usability Evaluation of in-Vehicle AR-HUD Interface Applying AHP-GRA

Abstract

Usability is regarded to be a fundamental requirement for in-vehicle HMIs, and usability evaluation reflects the impact of the interface and the acceptance from the users. This study introduced a usability evaluation model of AR-HUD interface by applying grey analytic hierarchy process (AHP). First, based on the ameliorated PSSUQ (Post-Study System Usability Questionnaire), the usability evaluation system was modified and optimized according to the characteristics of AR-HUD. On this basis, the preference weights for evaluation indexes were calculated by AHP and the idea of group decision. Finally, the criteria of usability were integrated into grey relational degree by applying grey relational analysis (GRA) to obtain optimal design. A case study was conducted to demonstrate the applicability of the developed model to the usability evaluation of AR-HUD interface design. According to the existing AR-HUD interface design, 7 dimensions of design elements (A-G) and 18 interface prototypes (S1-S18) were selected by Taguchi orthogonal array test (TOAT). As the results indicated, the grey relational degree of S5 was 0.923, signifying that it was the optimal sample; and the results were also compared with entropy-TOPSIS to verify the feasibility of the proposed method. The grey-based AHP evaluation model can be used to evaluate the usability level of AR-HUD interface effectively, which may help designers achieve insights for design process and samples decision-making.

Hesitant and uncertain linguistics based executive decision making using risk and regret aversion: Methods, implementation and analysis

“…Apart from these, decision making is one such important aspect in the practical scenario (e.g., such as in usability confirmation of augmented reality in-car systems [14] ), when considering human-centric systems which are able to analyze and subsequently emulate the human cognition and human behaviour, often in the presence of uncertainty and bounded rationality. Such concepts are much more evident in the recent works where researchers survey various models and methods from operations research (OR) and machine learning (ML) communities under the umbrella of contextual stochastic optimization, aiming to enhance understanding and encourage advancements in combining prediction algorithms and optimization techniques for decision-making under uncertainty [15] , whereas authors of [16] , explore the role of decision-making to assess hoteliers' attitudes and preferences towards using AI across various decision-making approaches, identifying clusters of managers based on their acceptance of AI technologies and determining factors influencing their preferences, ultimately highlighting the suitability of AI for specific types of decisions within the hotel industry regardless of personal or property-related characteristics.…”