Jane Huang
University of Notre Dame, IN·Computer Science
Groups of drones that fly and coordinate on their own, called autonomous drone swarms, could help responders handle disasters, monitor wildfires, assess infrastructure damage and track fast-changing environments. In these settings, small teams of people may have to watch over many drones at once and make quick decisions under stress.
- Fields
Computer Science
Engineering
Psychology
- Topics
human-robot interaction
human-computer interaction
drone swarms
- Grants
- 1 known grant, through Sep 2029
What they work on
- Design human-centered interfaces that help teams supervise autonomous drone swarms in time-critical tasks such as disaster response, using cognitive work analysis and iterative user-centered design.
- Measure how supervisors maintain awareness, coordinate, rely on automation and intervene, using behavioral measures and biometric sensing across simulation, remote operation and field deployment.
- Adapt automated stress-testing, computational models of human behavior and uncertainty-aware evaluation to discover failures in human supervision of drone swarms that standard user studies may overlook.
- Validate the discovered vulnerabilities in experiments with human supervisors and reduce them through repeated interface redesign, producing shared datasets and evaluation protocols.
More about the research
Current tools often make it hard for people to understand what the drones are doing, coordinate with each other, and know when to step in. This project will design and test interfaces that help teams supervise drone swarms safely in time-critical situations.
Why it matters
Emergency responders, wildfire monitors and infrastructure inspectors may increasingly rely on autonomous drone swarms. Interfaces that help people understand drone behavior, trust automation appropriately and intervene in time could make these operations safer and more reliable. The project's datasets and testing protocols could also guide the design of supervision tools for other autonomous multi-agent systems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
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Southern Methodist University, TX·Computer Science
Develop an AI curriculum covering federated learning, explainable AI, homomorphic encryption and neural networks, taught through lectures, exercises and simulations and shared via the NAIRR Pilot.
Shahram Ghandeharizadeh
University of Southern California, CA·Computer Science
Configure Flying Light Speck microdrones with lights bright enough to render the drones themselves invisible while flying and while tolerating touch such as poking.
Areen Alsaid
University of Michigan-Dearborn, MI·Engineering
Develop a framework for how driver trust transfers between automation levels and driving contexts, using survey studies that also assess the role of environmental factors.
The school
University of Notre Dame: 19 labs on PI Pages, $346M a year on research, 277 research doctorates a year.
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