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Award Detail

Doing Business As Name:University of Connecticut
  • Fei Miao
  • (215) 421-6608
Award Date:02/24/2021
Estimated Total Award Amount: $ 509,573
Funds Obligated to Date: $ 87,502
  • FY 2021=$87,502
Start Date:06/01/2021
End Date:05/31/2026
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles
Federal Award ID Number:2047354
DUNS ID:614209054
Parent DUNS ID:004534830
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Linda Bushnell
  • (703) 292-8950

Awardee Location

Street:438 Whitney Road Ext.
County:Storrs Mansfield
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Connecticut
Street:159 Discovery Lane
County:Storrs Mansfield
Cong. District:02

Abstract at Time of Award

The rapid evolution of ubiquitous sensing, communication, and computation technologies has contributed to the revolution of cyber-physical systems (CPS). Learning-based methodologies are integrated to the control of physical systems and demonstrating impressive performance in many CPS domains and connected and autonomous vehicles (CAVs) system is one such example with the development of vehicle-to-everything communication technologies. However, existing literature still lacks understanding of the tridirectional relationship among communication, learning, and control. The main challenges to be solved include (1) how to model dynamic system state and state uncertainties with shared information, (2) how to make robust learning and control decisions under model uncertainties, (3) how to integrate learning and control to guarantee the safety of networked CPS, and (4) how to quantify the benefits of communication. To address these challenges, this CAREER proposal aims to design integrated communication, learning, and control rules that are robust to hybrid system model uncertainties for safe operation and system efficiency of CAVs. The key intellectual merit is the design of integrated distributionally robust multi-agent reinforcement learning (DRMARL) and control framework with rigorous safety guarantees, considering hybrid system state uncertainties predicted with shared information, and the development of scientific foundation for analyzing and quantifying the benefits of communication. The fundamental theory and algorithm principles will be validated using simulators, small-scale testbeds, and full-scale CAVs field demonstrations, to form a new framework for future connectivity, learning, and control of CAVs and networked CPS. The technical contributions are as follows. (1). With shared information, we will design a cooperative prediction algorithm to improve hybrid system state and model uncertainty representations needed by learning and control. (2). Given enhanced prediction, we will design an integrated DRMARL and control framework with rigorous safety guarantee, and a computationally tractable algorithm to calculate the hybrid system decision-making policy. This integrates the strengths of both learning and control to improve system safety and efficiency. (3). We will define formally and quantify the value of communication given and propose a novel learn to communicate approach, to utilize learning and control to improve the communication actions. This project will also integrate an educational plan with the research goals by developing a learning platform of ``ssCAVs'' as an education tool and new interdisciplinary courses on “learning and control”, undertaking outreach to the general public and K-12 students and teachers, and directly involving high-school scholars, undergraduate and graduate students in research. This project is in response to the NSF CAREER 20-525 solicitation. 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.

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