Task-Allocation Decisions of Human-UAS Collaboration: Effects of Workload,Trust,and Self-confidence

Abstract

Uncrewed Aerial Systems (UAS) show promise in urban air transport, package delivery, and emergency services. UAS efficiency can be significantly improved by having multiple operators (m) managing a greater number of vehicles (N), or the m:N architecture of operation. The current study investigates how workload affects operators’ task-allocation decision-making and the potential mediating effects of two crucial human factors, trust and self-confidence. In the context of a simulated UAS package-delivery task under the m:N architecture, two groups of participants with different levels of expertise in UAS operation will be recruited: UAS pilots and university students. Each participant will watch two sets of videos with different work-load manipulations and report their preferred task-allocation strategy for various subtasks. Measures of perceived workload, trust, and self-confidence will be conducted after each video session. Findings will inform optimizing task-allocation designs for UAS missions, considering operators’ decision-making needs and expertise disparities.

Keywords

advanced air mobility human autonomy teaming uncrewed aerial systems

With the advancement in aviation technology, there has been an increasing discussion of Advanced Air Mobility (AAM) and Human-Autonomy Teaming (HAT), which involves collaboration between human operators and automated systems (Chancey et al., 2021). Motivated by the potential applications of AAM, this study focuses on investigating the human factors in HAT where multiple uncrewed aerial systems (UAS) and operators interact, using a package delivery task as the experimental context.

UAS efficiency improves with multiple operators (m) managing many vehicles (N), known as the m:N architecture (Chandarana et al., 2022). This setup allows the physical separation of operators from vehicles and task delegation among operators (Chancey & Politowicz, 2020). However, questions arise about operators’ decisions on task delegation, their preference for automated task assignment, and factors influencing these decisions. Tvaryanas (2006) suggested that operators delegate tasks because they need to rest, their workload becomes too high, or different operators are specialized in different tasks. Additionally, Chandarana et al. (2022) found that operators are more likely to hand off tasks to other operators when there are more contingencies and when handoff assistance tools are available. This result is consistent with the subject ratings of workload, as participants reported higher levels of workload with more contingencies (Chandarana et al., 2022).

Operators’ self-confidence affects their usage of automation. Patton (2023) suggested that people who had high self-confidence preferred to keep automation off. Meanwhile, trust calibration (i.e., appropriate trust levels matching system capabilities) is also vital for effective HAT (Bobko et al., 2022; Chen et al., 2021). Although a low level of trust was found to be associated with low team performance (McNeese et al., 2021), overtrust can lead to complacency and overreliance on unreliable systems (Lee & See, 2004).

Given the importance of human factors in m:N configurations, this study examines how workload affects task-allocation decisions and the mediating effects of trust and self-confidence.

A total of 20 undergraduates and 20 experts (those with FAA-certified pilot licenses for small UAS) will be recruited. The experiment will use videos created with AirSim (https://microsoft.github.io/AirSim) and QGroundControl (https://docs.qgroundcontrol.com), depicting package delivery tasks. The experiment will use a mixed design of 2 (number of UAS: 6 vs. 12 drones; within-subject) × 2 (expertise levels: students vs. experts; between-subject).

Participants will watch videos of drones performing delivery tasks and indicate their task allocation strategies. After each video, participants will rate their workload, trust in automation, and self-confidence using the NASA Task Load Index (Hart & Staveland, 1988), the Trust in Automation Questionnaire (Jian et al., 2000), and a 7-point, Likert-style question. Participants’ task-allocation decisions (manual or automated) will be collected.

A mixed factorial Analysis of Variance (ANOVA) will assess the effects of UAS number and expertise on task-allocation decisions. Mediation analysis will be used to examine if trust and self-confidence mediate the relationship between workload and task-allocation decisions. We expect higher workload ratings for the 12-drone scenarios compared to the 6-drone scenarios (Smith et al., 2021). We also expect a higher preference for automation in task-allocation decisions in the 12-drone scenarios than the 6-drone scenarios. Additionally, we expect both trust and self-confidence to be significant mediators of the relationship between the number of drones and task-allocation decisions.

Our findings are expected to inform task-allocation designs for UAS operations. Insights from this study will guide the development of effective decision-making tools and strategies for human operators, contributing to overall UAS mission efficiency. Additionally, the potential differences revealed between experts and novices in workload, trust, self-confidence, and decision-making will inform future tailored training programs.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is funded by the National Aeronautics and Space Administration under grant # 80NSSC23M0219.

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