Lack of support for handling a reduction of autonomy in a highly autonomous automation may lead to a stressful situation for a human when forced to take over. We present a design approach, the Reduced Autonomy Workspace, to address this. The starting point is that the human and the automation work together in parallel control processes, but at different levels of autonomy cognitive control, such as setting goals or implementing plans, which is different from levels of automation. When autonomy is reduced, the automation should consult the human by providing information that has been aligned to the level at which the human is working, and the timing of the provision should be adapted to suit the human’s work situation. This is made possible by allowing the automation to monitor the human in a separate process. The combination of these processes, information level alignment and timing of the presentation, are the key characteristics of the Reduced Autonomy Workspace. The Reduced Autonomy Workspace consists of four phases: Identification of the need; evaluation of whether, and, if so, when, and how to present information; perception and response by the human; implementation of a solution by the automation. The timing of the information presentation should be adapted in real-time to provide flexibility, while the level of the information provided should be tuned offline and kept constant to provide predictability. Use of the Reduced Autonomy Workspace can reduce the risk for surprising, stressful hand-over situations, and the need to monitor the automation to avoid them.
We evaluate visualization concepts to represent missing values in parallel coordinates. We focus on the trade‐off between the ability to perceive missing values and the concept's impact on common tasks. For this purpose, we identified three missing value representation concepts: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values. For the missing values axis and imputed values concepts, we additionally add downplay and highlight variations. We performed a crowd‐sourced, quantitative user study with 732 participants comparing the concepts and their variations using five real‐world datasets. Based on our findings, we provide suggestions regarding which visual encoding to employ depending on the task at focus.
In this article, we present a digital platform for Unmanned Traffic Management, UTM City, for research on visualization, simulation, and management of autonomous urban vehicle traffic. Such vehicles orient themselves automatically and provide services ranging from transport to remote presence and surveillance, and new regulations and standards for authorization and monitoring are currently being developed to accommodate for such services. Our system has been developed in close collaboration with domain experts that have contributed with scenarios and participated in numerous workshops to explore the use of visualization in airborne drone traffic monitoring, management, and development of the air space. We share here our experiences with this system and explore the needs for visualization in future scenarios to ensure safe, free, and efficient air spaces.
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