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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF WYOMING
Doing Business As Name:University of Wyoming
PD/PI:
  • Dongliang Duan
  • (307) 766-6541
  • dduan@uwyo.edu
Award Date:09/18/2019
Estimated Total Award Amount: $ 236,187
Funds Obligated to Date: $ 236,187
  • FY 2019=$236,187
Start Date:10/01/2019
End Date:09/30/2022
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CPS: Medium: Collaborative Research: Collective Intelligence for Proactive Autonomous Driving (CI-PAD)
Federal Award ID Number:1932139
DUNS ID:069690956
Parent DUNS ID:069690956
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Sandip Roy
  • (703) 292-7096
  • saroy@nsf.gov

Awardee Location

Street:1000 E. University Avenue
City:Laramie
State:WY
ZIP:82071-2000
County:Laramie
Country:US
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of Wyoming
Street:1000 E University Ave
City:LARAMIE
State:WY
ZIP:82071-2000
County:Laramie
Country:US
Cong. District:00

Abstract at Time of Award

The aim of this project is to develop real-time situational awareness that is shared via vehicle-to-vehicle (V2V) and vehicle-to-network (V2X). The approach is to combine the perception of sensors with interpretation of their situation to enable safer decisions, and take into account the limitations of the communication between vehicles and infrastructure. A highway system that supports autonomous and self-driven vehicles will include infrastructure sensors and onboard vehicle sensors, with massive connectivity among them and distributed intelligence across the entire transportation network. The resulting collective intelligence is one where autonomous vehicles serve as mobile sensors that augment one another along with fixed infrastructure sensors, to construct a real-time picture of traffic. This real-time picture is used to develop proactive driving actions that optimize traffic flow and minimize accident risk. The broader impacts include focused mentoring of undergraduate students who are interested in careers that require graduate training, to broaden participation in the fields of computing and engineering. The researchers organize an interdisciplinary project in signal processing and machine learning, control and optimization, communication and network science. The collective intelligence framework for proactive driving includes the following modules: 1) Scene Construction, consisting of signal processing and machine learning for constructing a representation of the driving environment from multi-modal multi-view sensors; 2) Situational Interpretation, consisting of driving environment dynamic analysis at progressive levels; 3) Decision Making, consisting of optimization and control to support proactive driving for safety and optimized flow; and 4) A Failsafe Network, consisting of communication and network science that supports optimized traffic flow under nominal conditions of sensing and communication, and moderated flow under conditions of compromised sensing and communication. 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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