Award Abstract # 2227338
BRITE Fellow: Autonomous Systems that Accommodate Human Perception and Reasoning about Uncertainty

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF NEW MEXICO
Initial Amendment Date: December 2, 2022
Latest Amendment Date: December 2, 2022
Award Number: 2227338
Award Instrument: Standard Grant
Program Manager: Marcello Canova
mcanova@nsf.gov
 (703)292-2576
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: May 1, 2023
End Date: April 30, 2028 (Estimated)
Total Intended Award Amount: $994,988.00
Total Awarded Amount to Date: $994,988.00
Funds Obligated to Date: FY 2023 = $994,988.00
History of Investigator:
  • Meeko Oishi (Principal Investigator)
    oishi@unm.edu
Recipient Sponsored Research Office: University of New Mexico
1 UNIVERSITY OF NEW MEXICO
ALBUQUERQUE
NM  US  87131-0001
(505)277-4186
Sponsor Congressional District: 01
Primary Place of Performance: University of New Mexico
1700 LOMAS BLVD NE STE 2200
ALBUQUERQUE
NM  US  87106-3837
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): F6XLTRUQJEN4
Parent UEI:
NSF Program(s): BRITE-BoostRschIdeasTransEquit
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 030E, 034E, 8024, 9102
Program Element Code(s): 192Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Fellow award will fund research that enables predictably safe interaction between autonomous systems and humans in uncertain environments, with applications in aerospace, manufacturing, transportation, and healthcare systems, thereby promoting the progress of science and advancing the national prosperity, welfare, and defense. By sensing and reacting to their environment, autonomous systems have the potential to help human operators accomplish difficult, dangerous, or risky tasks more easily and safely. However, when autonomous systems appear to be unpredictable, unreliable, or unresponsive, they become hindrances, rather than a source of help. To prevent this outcome, this project aims to develop an algorithmic design and control framework for autonomous systems that successfully accommodates the inevitable unpredictability of human actions, as well as the effects on human decision-making of uncertainty in the dynamics and action of the system. A key element of this framework is its ability to capture typical needs of humans to design solutions from uncertain information, and under potentially conflicting constraints, which meet desired objectives to the greatest possible degree. This research is integrated with curricular initiatives aiming to promote culturally responsive pedagogy and project-based learning in the training of students from diverse backgrounds in human-centric design of autonomous systems.

This research aims to make fundamental contributions to a stochasticity-based methodological approach for integrating knowledge of human perception and reasoning about uncertainty into the design and control of autonomous dynamical systems. To this end, new mathematical theory and computational algorithms will be developed, based in control theory, machine learning, and human factors. Such theory will address the handling of arbitrary, non-Gaussian forms of stochasticity and rational and non-rational decision models, with emphasis on computationally efficient controller synthesis. The feasibility and mathematical properties of methods to constructively accommodate human variability, and to be context-aware, will also be explored. The developed algorithms and theories will be experimentally validated in simulation-based platforms. This project enables the principal investigator to leverage expertise in methods for assuring probabilistic safety in stochastic dynamical systems and ongoing work on integrating cognitive models in autonomous systems to pursue a high-risk vision for human-centric autonomy with significant potential for transformational impact. It lays a foundation for advancing culturally responsive teaching and research practices throughout the engineering community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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DiPirro, Rachel and Sandhaus, Hauke and Goedicke, David and Calderone, Dan and Oishi, Meeko and Ju, Wendy "Characterizing Cultural Differences in Naturalistic Driving Interactions" , 2024 https://doi.org/10.1109/ITSC58415.2024.10919603 Citation Details
Ortiz, Kendric and DiPirro, Rachel and Thorpe, Adam J and Oishi, Meeko "Online Learning of Dynamical Systems Using Low-Rank Updates to Physics-Informed Kernel Distribution Embeddings" , 2024 https://doi.org/10.1109/CDC56724.2024.10885815 Citation Details

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