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Research Spending & Results

Award Detail

Doing Business As Name:University of Wisconsin-Madison
  • Soyoung Ahn
  • (608) 265-9067
  • John D Lee
  • Dan Negrut
Award Date:09/16/2019
Estimated Total Award Amount: $ 1,200,000
Funds Obligated to Date: $ 562,295
  • FY 2019=$562,295
Start Date:10/01/2019
End Date:09/30/2023
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:CPS: TTP Option: Medium: Identifying, Characterizing, and Shaping Multi-Scale Cyber-Human Interactions in Mixed Autonomous/Conventional Vehicle Traffic
Federal Award ID Number:1739869
DUNS ID:161202122
Parent DUNS ID:041188822
Program:CPS-Cyber-Physical Systems
Program Officer:
  • Ralph Wachter
  • (703) 292-8950

Awardee Location

Street:21 North Park Street
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Wisconsin-Madison
Street:1415 Engineering Drive
Cong. District:02

Abstract at Time of Award

The promise of intelligent transportation systems is safety, mobility, and efficiencies well beyond current technologies. Yet, transportation engineers and planners must anticipate future transportation needs for which the enabling technologies are rapidly evolving while the change-over from old to new transportation technologies will take decades. This project seeks to understand the impact of these trends for transportation engineering, offer approaches for effective shared control between autonomous vehicles and drivers, and provide insight into strategies to realize the promise of intelligent transportation systems. The approach to studying traffic uses driving simulators, built on a high-performance computing architecture, that enables high-resolution, physics-based, realistic, interactive simulations, necessary for detailed modeling of vehicles and drivers under changing traffic conditions and various assumptions on autonomy, control, and connectedness, and for monitoring human driving behaviors. This project identifies, characterizes, and shapes human cyber-physical interactions, bound to emerge in future mixed autonomous and conventional vehicle traffic. Its aims are to: (1) better understand human-cyber-physical interactions in mixed traffic, particularly driver trust in vehicle automation; (2) develop analytical and computational methods to assess the impacts on traffic capacity and regime transition for scenarios in which a driver switches from autonomous vehicle mode to conventional vehicle mode (e.g., loss of trust in automation scenarios); and (3) develop engineering solutions to enhance cyber-human interactions and traffic flow. The research embraces a multi-scale analysis approach anchored in human behavior analysis and interface design, modeling of mixed traffic and vehicle control, and system-level analysis of interdependency among human, vehicles, and traffic. Understanding of human cyber-physical interactions through this project will enable better future designs of autonomous vehicles and the ways in which humans interact with these evolving systems safely and dependably. 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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