457 research outputs found

    Transdisciplinarity seen through Information, Communication, Computation, (Inter-)Action and Cognition

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    Similar to oil that acted as a basic raw material and key driving force of industrial society, information acts as a raw material and principal mover of knowledge society in the knowledge production, propagation and application. New developments in information processing and information communication technologies allow increasingly complex and accurate descriptions, representations and models, which are often multi-parameter, multi-perspective, multi-level and multidimensional. This leads to the necessity of collaborative work between different domains with corresponding specialist competences, sciences and research traditions. We present several major transdisciplinary unification projects for information and knowledge, which proceed on the descriptive, logical and the level of generative mechanisms. Parallel process of boundary crossing and transdisciplinary activity is going on in the applied domains. Technological artifacts are becoming increasingly complex and their design is strongly user-centered, which brings in not only the function and various technological qualities but also other aspects including esthetic, user experience, ethics and sustainability with social and environmental dimensions. When integrating knowledge from a variety of fields, with contributions from different groups of stakeholders, numerous challenges are met in establishing common view and common course of action. In this context, information is our environment, and informational ecology determines both epistemology and spaces for action. We present some insights into the current state of the art of transdisciplinary theory and practice of information studies and informatics. We depict different facets of transdisciplinarity as we see it from our different research fields that include information studies, computability, human-computer interaction, multi-operating-systems environments and philosophy.Comment: Chapter in a forthcoming book: Information Studies and the Quest for Transdisciplinarity - Forthcoming book in World Scientific. Mark Burgin and Wolfgang Hofkirchner, Editor

    How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

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    This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

    A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

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    The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications

    Personalizing steering experience using steer-by-wire systems

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    The Digital Transformation of Automotive Businesses: THREE ARTEFACTS TO SUPPORT DIGITAL SERVICE PROVISION AND INNOVATION

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    Digitalisation and increasing competitive pressure drive original equipment manufacturers (OEMs) to switch their focus towards the provision of digital services and open-up towards increased collaboration and customer integration. This shift implies a significant transformational change from product to product-service providers, where OEMs realign themselves within strategic, business and procedural dimensions. Thus, OEMs must manage digital transformation (DT) processes in order to stay competitive and remain adaptable to changing customer demands. However, OEMs aspiring to become participants or leaders in their domain, struggle to initiate activities as there is a lack of applicable instruments that can guide and support them during this process. Compared to the practical importance of DT, empirical studies are not comprehensive. This study proposes three artefacts, validated within case companies that intend to support automotive OEMs in digital service provisioning. Artefact one, a layered conceptual model for a digital automotive ecosystem, was developed by means of 26 expert interviews. It can serve as a useful instrument for decision makers to strategically plan and outline digital ecosystems. Artefact two is a conceptual reference framework for automotive service systems. The artefact was developed based on an extensive literature review, and the mapping of the business model canvas to the service system domain. The artefact intends to assist OEMs in the efficient conception of digital services under consideration of relevant stakeholders and the necessary infrastructures. Finally, artefact three proposes a methodology by which to transform software readiness assessment processes to fit into the agile software development approach with consideration of the existing operational infrastructure. Overall, the findings contribute to the empirical body of knowledge about the digital transformation of manufacturing industries. The results suggest value creation for digital automotive services occurs in networks among interdependent stakeholders in which customers play an integral role during the services’ life-cycle. The findings further indicate the artefacts as being useful instruments, however, success is dependent on the integration and collaboration of all contributing departments.:Table of Contents Bibliographic Description II Acknowledgment III Table of Contents IV List of Figures VI List of Tables VII List of Abbreviations VIII 1 Introduction 1 1.1 Motivation and Problem Statement 1 1.2 Objective and Research Questions 6 1.3 Research Methodology 7 1.4 Contributions 10 1.5 Outline 12 2 Background 13 2.1 From Interdependent Value Creation to Digital Ecosystems 13 2.1.1 Digitalisation Drives Collaboration 13 2.1.2 Pursuing an Ecosystem Strategy 13 2.1.3 Research Gaps and Strategy Formulation Obstacles 20 2.2 From Products to Product-Service Solutions 22 2.2.1 Digital Service Fulfilment Requires Co-Creational Networks 22 2.2.2 Enhancing Business Models with Digital Services 28 2.2.3 Research Gaps and Service Conception Obstacles 30 2.3 From Linear Development to Continuous Innovation 32 2.3.1 Digital Innovation Demands Digital Transformation 32 2.3.2 Assessing Digital Products 36 2.3.3 Research Gaps and Implementation Obstacles 38 3 Artefact 1: Digital Automotive Ecosystems 41 3.1 Meta Data 41 3.2 Summary 42 3.3 Designing a Layered Conceptual Model of a Digital Ecosystem 45 4 Artefact 2: Conceptual Reference Framework 79 4.1 Meta Data 79 4.2 Summary 80 4.3 On the Move Towards Customer-Centric Automotive Business Models 83 5 Artefact 3: Agile Software Readiness Assessment Procedures 121 5.1 Meta Data 121 5.2 Meta Data 122 5.3 Summary 123 5.4 Adding Agility to Software Readiness Assessment Procedures 126 5.5 Continuous Software Readiness Assessments for Agile Development 147 6 Conclusion and Future Work 158 6.1 Contributions 158 6.1.1 Strategic Dimension: Artefact 1 158 6.1.2 Business Dimension: Artefact 2 159 6.1.3 Process Dimension: Artefact 3 161 6.1.4 Synthesis of Contributions 163 6.2 Implications 167 6.2.1 Scientific Implications 167 6.2.2 Managerial Implications 168 6.2.3 Intelligent Parking Service Example (ParkSpotHelp) 171 6.3 Concluding Remarks 174 6.3.1 Threats to Validity 174 6.3.2 Outlook and Future Research Recommendations 174 Appendix VII Bibliography XX Wissenschaftlicher Werdegang XXXVII Selbständigkeitserklärung XXXVII

    A next generation manufacturing control system for a lean production environment

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    This thesis focuses on addressing the need for a new approach to the design and implementation of manufacturing control systems for the automotive industry and in particular for high volume engine manufacture. Whilst the operational domain in the automotive industry has moved to lean production techniques, the design of presentday manufacturing control systems is still based on systems intended for use in a mass production environment. The design and implementation of current manufacturing control systems is therefore inappropriate when viewed from a business context. The author proposes that it is possible to create a more appropriate manufacturing control systems based on an optimised use of advanced manufacturing technology within the complete business context. Literature is reviewed to provide a detailed understanding of the relationship between modem operating practices and the application of contemporary control systems. The primary tasks of manufacturing control systems, within the context of a structured systems approach to manufacturing technology, production management and industrial economics are identified. A study of modem manufacturing control system technology is carried out, highlighting the fundamental principles that influence application engineering in this area. The thesis develops a conceptual design framework that aids the identification of attributes required of a next generation manufacturing control system (NGCS), in order to enhance the business performance of lean automotive manufacturing. The architecture for a next generation control system is specified and a Proof of concept system implemented. Potential advances over contemporary practice are identified with the aid of a practical implementation at a major automotive manufacturer

    License to Supervise:Influence of Driving Automation on Driver Licensing

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    To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation
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