Exploring religious tourist experiences in Jerusalem: The intersection of Abrahamic religions

By considering the importance of religious tourism for travel and the tourism industry, this study aims to identify religious tourists' experiences in Jerusalem, as one of the most important holy cities. By a survey, 848 data were collected from the Jewish, Christian, and Muslim religious tourists. Results showed that religious tourism experience was a multi-faceted construct, which consists of engaging mentally, discovering new things, interacting & belonging, connecting spiritually & emotionally, and relaxing & finding peace dimensions. By using these dimensions, perceived experience differences of tourists were examined depending on religion. Moreover, religious tourism experience was identified to significantly affect overall tourist satisfaction with Jerusalem. The study concluded with discussion of the findings and their implications

Using AI to Enable Design for Diversity: A Perspective

Inclusive design focuses on diversity. The contextualized user-sensitive design framework of the interaction system needs to analyze and deal with complex diversity factors, which challenges the traditional design process, tools, and methods. Therefore, new technological progress is needed to provide more innovation potential. The authors point out that the design process of smart products is evolving in response to uncertainty. In the future, diversity-oriented design will tend to allocate design resources and values in an algorithmic way rather than the compromised unity solution. This paper analyzes the limitations and potential of the application of AI technology represented by deep learning in diversity-oriented design practice and design research, puts forward the goal and direction of further research, and discusses the critical links of AI-enabled diversity design in interdisciplinary research environment

Designing Adaptation in Cars: An Exploratory Survey on Drivers’ Usage of ADAS and Car Adaptations

Current production cycle cars offer a wide range of driver assistance features spanning from Advanced Driver Assistance Systems to more established systems such as wing mirrors. All these features allow an increasing amount of adaptation enabling the driver to tailor all them to his or her requirements. However, drivers’ usage of and attitude towards these features as well as their possible adaptations are largely unexplored and, as a consequence, not well understood. We present an exploratory survey on this topic and apply an inclusive design approach in order to accommodate the whole range of diversity in our population. The results indicate a low usage rate of driver assistance features as well as their possible adaptations. However, results suggest a high appreciation for a potential smart adaptation of driver assistance features

Intelligent driver profiling system for cars – a basic concept

Many industries have been transformed by the provision of service solutions characterised by personalisation and customisation - most dramatically the development of the iPhone. Personalisation and customisation stand to make an impact on cars and mobility in comparable ways. The automobile industry has a major role to play in this change, with moves towards electric vehicles, autonomous cars, and car sharing as a service. These developments are likely to bring disruptive changes to the business of car manufacturers as well as to drivers. However, in the automobile industry, both the user’s preferences and demands and also safety issues need to be confronted since the frequent use of different makes and models of cars, implied by car sharing, entails several risks due to variations in car controls depending on the manufacturer. Two constituencies, in particular, are likely to experience even more difficulties than they already do at present, namely older people and those with capability variations. To overcome these challenges, and as a means to empower a wide car user base, the paper here presents a basic concept of an intelligent driver profiling system for cars: the system would enable various car characteristics to be tailored according to individual driver-dependent profiles. It is intended that wherever possible the system will personalise the characteristics of individual car components; where this is not possible, however, an initial customisation will be performed

Designing Adaptation in Cars: An Exploratory Survey on Drivers’ Usage of ADAS and Car Adaptations

Current production cycle cars offer a wide range of driver assistance features spanning from Advanced Driver Assistance Systems to more established systems such as wing mirrors. All these features allow an increasing amount of adaptation enabling the driver to tailor all them to his or her requirements. However, drivers’ usage of and attitude towards these features as well as their possible adaptations are largely unexplored and, as a consequence, not well understood. We present an exploratory survey on this topic and apply an inclusive design approach in order to accommodate the whole range of diversity in our population. The results indicate a low usage rate of driver assistance features as well as their possible adaptations. However, results suggest a high appreciation for a potential smart adaptation of driver assistance features

Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data

Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus crucial for delivering adaptive human machine interaction solutions with reduced impact on the primary task of driving. In this paper, we tackle the problem of workload estimation from driving performance data. First, we present a novel on-road study for collecting subjective workload data via a modified peripheral detection task in naturalistic settings. Key environmental factors that induce a high mental workload are identified via video analysis, e.g. junctions and behaviour of vehicle in front. Second, a supervised learning framework using state-of-the-art time series classifiers (e.g. convolutional neural network and transform techniques) is introduced to profile drivers based on the average workload they experience during a journey. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload. This computationally efficient and flexible method can be easily personalised to a driver (e.g. incorporate their inferred average workload profile), adapted to driving/environmental contexts (e.g. road type) and extended with data streams from new sources. The efficacy of the presented profiling and instantaneous workload estimation approaches are demonstrated using the on-road study data, showing $F_{1}$ scores of up to 92% and 81%, respectively

OESDs in an on-road study of semi-automated vehicle to human driver handovers

Design of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research