| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | March 5, 2019 |
| Latest Amendment Date: | March 5, 2019 |
| Award Number: | 1902006 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Shen
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | June 1, 2019 |
| End Date: | May 31, 2023 (Estimated) |
| Total Intended Award Amount: | $130,000.00 |
| Total Awarded Amount to Date: | $130,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1000 HILLTOP CIR BALTIMORE MD US 21250-0001 (410)455-3140 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1000 Hilltop Circle Baltimore MD US 21250-0002 |
| 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): | 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
Emerging connected and autonomous vehicle (CAV) technologies offer great potentials to reduce traffic congestion and improve traffic efficiency. However, much of the CAV related work focuses on individual vehicles' safety, which compromises traffic efficiency when mixed traffic (CAVs and human-driven vehicles) are on the road interacting with each other. This project aims to study how a group of CAVs can respond to exogenous disturbances resulting from human-driven vehicles, lane change requests and abnormal traffic and cyber conditions through cooperative speed or acceleration control. The research will improve road safety and traffic efficiency of future transportation systems involving CAVs. This project will disseminate research and education outcomes to broader audiences, including under-represented college and K-12 students with a particular focus on minority students.
The specific research objectives of this project are to develop vehicle platoon centered optimal, adaptive, and resilient vehicle platooning control under various normal or abnormal traffic and/or cyber conditions. The project will develop (a) advanced model predictive control integrating distributed optimization for optimal vehicle platooning control under normal traffic/cyber conditions; (b) mixed integer programming based model predictive control for optimal vehicle platooning control adaptive to lane change requests; (c) resilient vehicle platooning control integrating real-time learning and distributed optimization under abnormal traffic and/or cyber conditions. The project will integrate the state of the art from multiple fields including traffic flows, control, optimization, learning, and distributed computation and will establish an interdisciplinary foundation for coordinated and automated vehicle platoon centered traffic control under complex real-world traffic conditions.
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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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project aims to develop vehicle platoon centered optimal, adaptive, and resilient vehicle platooning control under possibly uncertain or abnormal traffic and/or cyber conditions. The outcomes and key findings are:
- Intellectual merit:
Three major research tasks have been carried out during 2019-2023:
1. Distributed computation based, platoon centered CAV platooning control. Particularly, fully distributed optimization based, platoon centered CAV platooning control are developed under both linear and nonlinear vehicle dynamics. Their development relies on several novel techniques from distributed optimization which will shed light on future research on distributed computation for CAV platooning. Further, nonlinear vehicle dynamics is considered in this task, leading to many new results in distributed computation and stability analysis that will impact on CAV platooning control under more complex and realistic traffic conditions.
2 . Adaptive platooning control in a complex traffic environment using machine learning and distributed computation techniques. This task develops adaptive platooning techniques for a CAV platoon in a complex traffic environment, which is often subject to complicated interactions between a CAV platoon and other vehicles, e.g., human driven vehicles. Related machine learning and distributed computation methods are also developed to aid a CAV platoon to handle complex traffic condition. Specific results include cooperative lane change and CAV platooning control using the hybrid MPC approach, column partition based fully distributed algorithms for convex sparse optimization based learning, and HT operator and sparse projection based algorithms for neural network pruning in real-time learning.
3. Resilient platooning control and planning subject to uncertainties and/or adversarial inputs. The following sub-tasks have been carried out: (i) mode-conscious stabilization of switched linear control systems against adversarial switchings, inspired by resilient control of a CAV platoon under malicious cyber attacks; (ii) uncertain dynamic user equilibrium problem for traffic planning and prediction on a general traffic network via the dynamic stochastic variational inequality approach; (iii) sequential feasibility analysis and constraint properties of a CAV platoon subject to uncertainties; and (iv) efficient distributed averaging algorithms for resilient CAV platooning.
This project advances connected and autonomous vehicle platooning technology and builds a solid foundation for coordinated and automated driving under complex traffic conditions. It also sheds light on automation technologies in future transportation infrastructure and cross-disciplinary research involving transportation, optimization and control, as well as real-time learning and computation in data science.
- Broader impacts:
The research in this project is highly interdisciplinary, exploiting a wide range of techniques from transportation, optimization, control to machine learning, distributed computation, and nonsmooth/uncertain systems. Impacts on other disciplines include distributed optimization and computation, switching/hybrid systems and multi-agent control systems, sparse optimization and machine learning, and stochastic and nonsmooth systems.
The research activities carried out in this project have led to 6 journal articles published in top-tier journals in transportation, optimization and control, along with another journal article under review, and 3 refereed conference papers published in major transportation conferences. Moreover, an undergraduate student research paper has been published supported by this grant. The PI and his PhD students have presented the research findings of this project at several seminars (invited talks), workshops, and conferences.
Three PhD students have been supervised by the PI in this project. Two of them have successfully defended their PhD theses, and one has passed the oral qualified exam and is working toward his PhD thesis.
The PI has supervised an undergraduate student to carry out a summer research project supported by this grant. This project provided this student a great opportunity to learn cutting-edge CAV technologies, gain hands-on research experience, and enhance his interest in STEM. This student entered the PhD program in Applied Math at Northwestern University with full scholarship after graduating from UMBC in 2021 and he won the NSF GRFP award in 2023.
The research in this project promotes seamless collaboration between transportation engineering and other emerging fields, such as big data and machine learning, to fully exploit the potentials of CAV technologies.
Last Modified: 08/29/2023
Modified by: Jinglai Shen
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