Caroline C. Hayes

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    Hand Images in Virtual Spatial Collaboration for Traffic Incident and Disaster Management
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-03) Drew, Daniel; Hayes, Caroline C.; Nguyen, Mai-Anh; Cheng, Xuan
    To develop demonstration technology that can overlay hand videos on spatial images such as traffic maps, and assess the impact of this technology on virtual collaboration. This work explores to what degree gestures impact collaboration effectiveness in the task of traffic incident management, with the goal of informing design of tools to support virtual collaboration in this domain. Methods: Eighteen participants worked in pairs to solve three traffic incident scenarios using three different interaction approaches: 1) face-to-face: participants worked together by marking up an electronic map projected on the table in front of them; 2) separated: participants were separated by a soft wall while they worked together on the electronic map with electronic drawing tools; or 3) hand images: same as 2 with the addition of the partner’s hand images projected on the map. Participants were video recorded. The questionnaires were given to participants after each trial to evaluate workload, positive interactions, team behaviors, connection to teammate, and frustration. Results: Participants spent more time on the task and perceived a higher level of time pressure when using hand images than when working face-to-face. When working face-to-face, participants felt more like their teammate was at the same table and felt less disconnected from their teammate than when working separately or using hand images. Conclusions: The results indicate that adding hand videos to a virtual drawing tool for the task of traffic incident management can increase team behaviors and change the way in which team members communicate information.
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    In-Vehicle Decision Support to Reduce Crashes at Rural Thru- Stop Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2011-08) Hayes, Caroline C.; Drew, Daniel
    Purpose: Within the context of thru-stop intersections, investigate the feasibility and future promise of warning systems inside the vehicle, where interfaces are best placed, and what modalities are most effective (visual versus haptic). Methods: A driving simulator study was conducted to compare three decision support systems (DSSs): a dynamic traffic sign, a set of displays on the vehicle side mirrors, and a vibrating seat. Dependent variables included measurements of safe driving behavior, and a usability questionnaire. A follow-up focus group study was conducted to gain further feedback on the in-vehicle systems and on ideas for how to improve the systems. Results: The vibrating seat yielded significantly higher results than the dynamic traffic sign on two safety variables. No system clearly outperformed the others in terms of promoting safer driving behavior, nor did any improve driving performance compared to the control condition. The questionnaire and usability data showed that the dynamic traffic sign was most preferred, while the in-vehicle displays were most comprehended. Comments during the simulator studies suggested that participants wanted stronger advisory messages from the systems, and the Focus Group Study confirms this. Conclusions: In-vehicle DSSs appear to be feasible for the purposes of assisting drivers with navigating rural thru-stop intersections. No results of this study indicate that in-vehicle systems are an inherently poor means of presenting traffic gap information to the driver. Results indicate that a visual display would be easier to comprehend than a vibrotactile display when no training or explanation is provided.
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    FOX-GA: A Genetic Algorithm for Generating and Analyzing Battlefield Courses of Action"
    (MIT Press, 1999) Schlabach, J.L.; Hayes, Caroline C.; Goldberg, D.E.
    This paper describes FOX-GA, a genetic algorithm (GA) that generates and evaluates plans in the complex domain of military maneuver planning. FOX-GA’s contributions are to demonstrate an effective application of GA technology to a complex real world planning problem, and to provide an understanding of the properties needed in a GA solution to meet the challenges of decision support in complex domains. Previous obstacles to applying GA technology to maneuver planning include the lack of efficient algorithms for determining the fimess of plans. Detailed simulations would ideally be used to evaluate these plans, but most such simulations typically require several hours to assess a single plan. Since a GA needs to quickly generate and evaluate thousands of plans, these methods are too slow. To solve this problem we developed an efficient evaluator (wargamer) that uses course-grained representations of this problem domain to allow appropriate yet intelligent trade-offs between computational efficiency and accuracy. An additional challenge was that users needed a diverse set of significantly different plan options from which to choose. Typical GA’s tend to develop a group of “best” solutions that may be very similar (or identical) to each other. This may not provide users with sufficient choice. We addressed this problem by adding a niching strategy to the selection mechanism to insure diversity in the solution set, providing users with a more satisfactory range of choices. FOX-GA’s impact will be in providing decision support to time constrained and cognitively overloaded battlestaff to help them rapidly explore options, create plans, and better cope with the information demands of modern warfare.
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    Creating Effective Decision Aids for Complex Tasks
    (Usability Professionals' Association, 2008-08) Hayes, Caroline C.; Akhavi, Farnaz
    Engineering design tasks require designers to continually compare, weigh, and choose among many complex alternatives. The quality of these selection decisions directly impacts the quality, cost, and safety of the final product. Because of the high degree of uncertainty in predicting the performance of alternatives while they are still just sketches on the drawing board, and the high cost of poor choices, mathematical decision methods incorporating uncertainty have long held much appeal for product designers, at least from a theoretical standpoint. Yet, such methods have not been widely adopted in practical settings. The goals of this work are to begin understanding why this is so and to identify future questions that may lead to solutions. This paper summarizes the results of several studies by the authors: two laboratory studies in which we asked product designers to use various mathematical models to compare and select design alternatives, and a set of ethnographic studies in which we observed product designers as they worked so that we could better understand their actual practices and needs during decision making. Based on these studies, we concluded that the mathematical models, as formulated, are not well suited to designers’ needs and approaches. We propose a research agenda for developing new approaches that combine decision theoretic and usercentered methods to create tools that can make product designers’ decision making work easi