| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | February 22, 2022 |
| Latest Amendment Date: | February 28, 2024 |
| Award Number: | 2150292 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Vladimir Pavlovic
vpavlovi@nsf.gov (703)292-8318 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | March 1, 2022 |
| End Date: | February 28, 2025 (Estimated) |
| Total Intended Award Amount: | $281,712.00 |
| Total Awarded Amount to Date: | $288,112.00 |
| Funds Obligated to Date: |
FY 2024 = $6,400.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
21000 W 10 MILE RD SOUTHFIELD MI US 48075-1051 (248)204-2103 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
21000 W 10 Mile Rd Southfield MI US 48075-1051 |
| 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): | RSCH EXPER FOR UNDERGRAD SITES |
| Primary Program Source: |
01002223DB NSF RESEARCH & RELATED ACTIVIT |
| 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): | 47.070 |
ABSTRACT
This project provides hands-on active learning opportunities for undergraduate students to conduct research for urban road self-driving functions using street-legal vehicles. It is uncommon for US undergraduate students to have opportunities to develop self-drive algorithms using real vehicles. In this research, students will analyze and evaluate the results of different algorithms after real-world testing. They will gain knowledge and confidence in self-driving algorithm development, share their knowledge with others through publications, and potentially choose autonomous vehicle research and development as their career path. Student perceptions about the smart mobility field and career options will be positively affected, leading to increased interest in future graduate studies. Evaluation and comparison results of various self-driving algorithms tested are original contributions to the research community. The results will benefit the advance of autonomous vehicle software development, bringing benefits to society including reduced pollution, less traffic accidents and congestion, and lower economic costs.
The specific objectives are to: (1) provide experiences to underrepresented undergraduate students who otherwise might not have research opportunities to learn fundamental theories in autonomous vehicle development; (2) allow students to design algorithms to practice software development using real vehicles on real test courses; (3) strengthen their confidence, self-guided capabilities, and research skills; and, (4) increase the number of students interested in graduate programs and ultimately provide a quality research and development workforce to industry. Activities include (1) intensive daily training workshops with hands-on problem solving tasks with real vehicles; (2) defining research problems; (3) design, implementation, and testing of algorithms; (4) analysis and evaluation of the collected data and results; (5) writing technical reports and presentations; (6) assessments before and after the program; (7) field trips; and (8) post-meetings for publications. This research also advances knowledge by identifying advantages and disadvantages of using real vehicles in teaching and learning self-driving algorithms, plus the most effective strategies to teach self-driving algorithms using real vehicles in order to improve undergraduate education.
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.
From 2022 to 2024, during the summer months, this collaborative Research Experience for Undergraduates (REU) program between Lawrence Technological University (LTU) and Michigan State University (MSU) provided 23 undergraduate students with hands-on research experience in developing and evaluating connected and autonomous self-driving algorithms using full-scale, street-legal electric vehicles. This program aimed to bridge the gap between simulation and real-world autonomous vehicle deployment while fostering student growth in technical proficiency, research skills, and academic and industry career readiness.
The program contributed to the field of connected and autonomous vehicle research by training students to develop and test self-driving algorithms beyond typical simulation and small-scale environments. The students worked with real-world vehicle platforms in an outdoor test track, addressing challenges in perception, control, and decision-making for connected and autonomous systems. Student teams developed and evaluated:
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A variety of lane-keeping algorithms, both hand-crafted and deep learning-based, were developed and tested for real-world navigation under dynamic conditions.
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Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2X) communication protocols using a team developed road side unit (RSU).
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A low-cost smart intersection controller enabling hazard avoidance and coordinated automated navigation.
Students engaged in iterative development, testing their algorithms in simulation before transitioning to real-world deployment. The program emphasized interdisciplinary learning by integrating computer vision, robotics, control theory, real-time systems, and software engineering through an intensive series of workshop modules. Participants also engaged in field trips to academic and industry partners to broaden their horizons, and non-technical instruction sessions focused on reading and writing academic papers and academic career pathways. The program was updated annually based on survey results, student-reported feedback, and instructor observations, with new workshop content, research projects, field trips, and social activities.
The technical findings of this program resulted in multiple peer-reviewed conference and journal publications, with students presenting their work at technical conferences run at MIT and elsewhere, and sponsored by IEEE and other professional organizations. The students' paper won first place in the 2023 IEOM International Conference on Smart Mobility and Vehicle Electrification "Smart Mobility" competition. The PIs of the collaborative award also published two peer-reviewed papers in ASEE and IEEE conferences relating to the program design and outcomes, and supported K12 outreach and public engagement through workshops, presentations, digital artifacts, demo days, and press releases. A total of seven papers have been published, and two more have recently been accepted.
This program was designed to offer opportunities to nontraditional students to nontraditional students, including those from non-research-intensive institutions, primarily undergraduate institutions (PUIs), and first-generation college students. Over three years, the program successfully increased student confidence in research, with measurable improvements in technical proficiency, research communication, and self-efficacy as evaluated by the program's own external evaluator and CRA CERP. Several students additionally went on to apply to graduate programs, including those who previously had not indicated such interest.
Last Modified: 05/05/2025
Modified by: Chan-Jin Chung
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