Browsing by Author "Wang, Tianzi"

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  • Automating context dependent gaze metrics for evaluation of laparoscopic surgery manual skills
    Deng, Shiyu; Kulkarni, Chaitanya; Parker, Sarah J.; Barnes, Laura E.; Wang, Tianzi; Hartman-Kenzler, Jacob; Safford, Shawn; Lau, Nathan (2022-03)
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    Collaborative Multi-Robot Multi-Human Teams in Search and Rescue
    Williams, Ryan K.; Abaid, Nicole; McClure, James; Lau, Nathan; Heintzman, Larkin; Hashimoto, Amanda; Wang, Tianzi; Patnayak, Chinmaya; Kumar, Akshay (2022-04-30)
    Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that yields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy.
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    Transparency, trust, and level of detail in user interface design for human autonomy teaming
    Wang, Tianzi (Virginia Tech, 2023-11-03)
    Effective collaboration between humans and autonomous agents can improve productivity and reduce risks of human operators in safety-critical situations, with autonomous agents working as complementary teammates and lowering physical and mental demands by providing assistance and recommendations in complicated scenarios. Ineffective collaboration would have drawbacks, such as risks of being out-of-the-loop when switching over controls, increased time and workload due to the additional needs for communication and situation assessment, unexpected outcomes due to overreliance, and disuse of autonomy due to uncertainty and low expectations. Disclosing the information about the agents for communication and collaboration is one approach to calibrate trust for appropriate reliance and overcome the drawbacks in human-autonomy teaming. When disclosing agent information, the level of detail (LOD) needs careful consideration because not only the availability of information but also the demand for information processing would change, resulting in unintended consequences on comprehension, workload, and task performance. This dissertation investigates how visualization design at different LODs about autonomy influences transparency, trust, and, ultimately, the effectiveness of human autonomy teaming (HAT) in search and rescue missions. LOD indicates the amount of information aggregated or organized in communication for the human to perceive, comprehend, and respond, and could be manipulated by changing the granularity of information in a user interface. High LOD delivers less information so that users can identify overview and key information of autonomy, while low LOD delivers information in a more detailed manner. The objectives of this research were (1) to build a simulation platform for a representative HAT task affected by visualizations at different LODs about autonomy, (2) to establish the empirical relationship between LOD and transparency, given potential information overload with indiscriminate exposure, and (3) examine how to adapt LOD in visualization with respect to trust as users interact with autonomy over time. A web-based application was developed for wilderness SAR, which can support different visualizations of the lost-person model, UAV path-planner, and task assignment. Two empirical studies were conducted recruiting human participants to collaborate with autonomous agents, making decisions on search area assignment, unmanned aerial vehicle path planning, and object detection. The empirical data included objective measures of task performance and compliance, subjective ratings of transparency, trust, and workload, and qualitative interview data about the designs with students and search and rescue professionals. The first study revealed that lowering LODs (i.e., more details) does not lead to a proportional increase in transparency (ratings), trust, workload, accuracy, and speed. Transparency increased with decreased LODs up to a point before the subsequent decline, providing empirical evidence for the transparency paradox phenomenon. Further, lowering LOD about autonomy can promote trust with diminishing returns and plateau even with lowering LOD further. This suggests that simply presenting some information about autonomy can build trust quickly, as the users may perceive any reasonable forms of disclosure as signs of benevolence or good etiquette that promote trust. Transparency appears more sensitive to LOD than trust, likely because trust is conceptually less connected to the understanding of autonomy than transparency. In addition, the impacts of LODs were not uniform across the human performance measurements. The visualization with the lowest LOD yielded the highest decision accuracy but the worst in decision speed and intermediate levels of workload, transparency, and trust. LODs could induce the speed-accuracy trade-off. That is, as LOD decreases, more cognitive resources are needed to process the increased amount of information; thus, processing speed decreases accordingly. The second study revealed patterns of overall and instantaneous trust with respect to visualization at different LODs. For static visualization, the lowest LOD resulted in higher transparency ratings than the middle and high LOD. The lowest LOD generated the highest overall trust amongst the static and adaptive LODs. For visualizations of all LODs, instantaneous trust increased and then stabilized after a series of interactions. However, the rate of change and plateau for trust varied with LODs and modes between static and adaptive. The lowest, middle, and adaptive LODs followed a sigmoid curve, while the high LOD followed a linear one. Among the static LODs, the lowest LOD exhibits the highest growth rate and plateau in trust. The middle LOD developed trust the slowest and reached the lowest plateau. The high LOD showed a linear growth rate until a level similar to that of the lowest LOD. Adaptive LOD earned the trust of the participants at a very similar speed and plateau as the lowest LOD. Taking these results together, more details about autonomy are effective for expediting the process of building trust, as long as the amount of information is carefully managed to prevent overloading participants' information processing. Further, varying quantities of information in adaptive mode could yield very similar growth and plateau in trust, helping humans to deal with either the minimum or maximum amount of information. This adaptive approach could prevent situations where comprehension is hindered due to insufficient information or where users are potentially overloaded by details. Adapting LODs to instantaneous trust presents a promising technique for managing information exchange that can promote the efficiency of communication for building trust. The contribution of this research to literature is two-fold. The first study provides the first empirical evidence indicating that the impact of LODs on transparency and trust is not linear, which has not been explicitly demonstrated in prior studies about HAT. The impact of LOD on transparency is more sensitive than trust, calling for a more defined and consistent use of the term or concept - "transparency" and a deeper investigation into the relationships between trust and transparency. The second study presents the first examination of how static and dynamic LODs can influence the development of trust toward autonomy. The algorithm for adapting LOD for the adaptive visualization based on user trust is novel, and adaptive LODs in visualization could switch between detailed and abstract information to influence trust without always transmitting all the details about autonomy. Visualizations with different LODs in both static and adaptive modes present their own set of benefits and drawbacks, resulting in trade-offs concerning the speed of promoting trust and information quantity transmitted during communication. These findings indicate that LOD is an important factor for designing and analyzing visualization for transparency and trust in HAT.