| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | February 8, 2019 |
| Latest Amendment Date: | February 8, 2019 |
| Award Number: | 1846795 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Daan Liang
dliang@nsf.gov (703)292-2441 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | August 1, 2019 |
| End Date: | September 30, 2025 (Estimated) |
| Total Intended Award Amount: | $500,000.00 |
| Total Awarded Amount to Date: | $500,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1400 TOWNSEND DR HOUGHTON MI US 49931-1200 (906)487-1885 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1400 Townsend Dr Houghton MI US 49931-1295 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CAREER: FACULTY EARLY CAR DEV, CIS-Civil Infrastructure Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
This Faculty Early Career Development Program (CAREER) award supports fundamental research needed to use real-time high-frequency data from many connected vehicles to automatically control driving and routing decisions. This research is interdisciplinary in nature as it requires the understanding of research and education approaches in various fields including transportation systems, operations research, control theory, vehicle dynamics, data science, and computer science. The project will contribute to training the next generation of scientists and engineers in multidisciplinary perspectives through curriculum development and research involvement. The PI will work closely with high school teachers and students, targeting underrepresented minorities, through a Summer Internship Program for first-generation college students and a Youth Development Mentoring Program using a set of online tools and case studies.
The project is to provide real-time, online, and predictive robust optimal driving and routing decisions to connected and automated vehicles. Ultimately, the project will help address traffic congestion in smart cities. This research will fill knowledge gaps in the integration of real-time high-frequency connected vehicle data into online robust automated driving and routing control systems. Specifically, this research will (1) suggest data-driven optimization-based model predictive control (MPC) models through the concepts of "forecasting the forecasts of others" and online learning for cooperative connected and automated driving under traffic uncertainty; (2) integrate n-person dynamic routing games and MPC models for non-cooperative connected and automated routing under player and capacity uncertainties; and (3) balance cooperative automated driving and non-cooperative automated routing through optimizing platoon formation to increase network efficiency. To address computational scalability issues in large scale networks, this research will also explore fictitious play type algorithms to solve the n-person dynamic routing games, as well as leverage distributed optimization to address computational issues in platoon formation problems. Simulation analysis and road testing will be conducted for feasibility analysis of the models using a fleet of connected and automated vehicles.
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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