Undergraduate Research Experiences for Automated and Connected  Vehicle Algorithm Development using Real Vehicles

Chan-Jin Chung; Joshua E Siegel; Mark Wilson

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Conference: 2024 ASEE North Central Section Conference
Location: Kalamazoo, Michigan
Publication Date: March 22, 2024
Start Date: March 22, 2024
End Date: March 23, 2024
Tagged Topic: Diversity
Page Count: 15
DOI: 10.18260/1-2--45645
Permanent URL: https://strategy.asee.org/45645
Download Count: 22

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Paper Authors

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Chan-Jin Chung Lawrence Technological University

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Chan-Jin “CJ” Chung is a professor of computer science at Lawrence Technological University with expertise in Intelligent Robotics, Artificial Intelligence (AI), Machine Learning, Deep Learning, Evolutionary Computation, and Computer Science and AI education. He was a senior research scientist at Electronics and Telecommunications Research Institute (ETRI) in South Korea where he was involved in developing a digital switching system called TDX that later became a base system for the first commercialized CDMA (2G) system in the world. His doctoral research at Wayne State University was the development of self-adaptive AI frameworks motivated by cultural evolution processes, which was then applied to solve various problems such as optimizing neural networks and 2D & 3D target shape optimization. He launched numerous world-wide STEM+CS/AI programs including Robofest, RoboParade, Vision Centric Challenge, RoboArts, and MathDance. He also mentored college robotics teams for IGVC, RoboCup soccer, and World Robot Olympiad. In 2011, IEEE honored Dr. Chung with its citation of honor award for his leadership in STEM education. His current projects using drive-by-wire vehicles include developing intelligent ground vehicle systems funded by US Army/GVSC and providing research opportunities in evaluating self-drive algorithms for undergraduates, funded by National Science Foundation (NSF).

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Joshua E Siegel Michigan State University

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Mark Wilson Michigan State University

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Abstract

We are experiencing a revolution in vehicle operation, with fully automated robotaxis deployed and available for public use in major U.S. markets in 2023. These vehicles, while imperfect, already are arguably safer than the average human driver. Despite this rapid progress, there remain significant research and development problems that must be addressed; beyond this, there is an underdeveloped workforce for skilled researchers, developers, and practitioners in these areas, a fact that may delay necessary advances. We have created and run for two years a National Science Foundation funded Research Experience for Undergraduates (NSF REU) focused on solving both unmet research needs, and workforce development and pipeline programs. In our REU, which makes use of simulation and two full-scale, street-legal drive-by-wire electric vehicles with perception, planning, and control capabilities, our primary goals include to (1) provide hands-on experiences to 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 and evaluation using real vehicles on real test courses, (3) strengthen their confidence, self-guided capabilities, and research skills, and (4) increase the number of students, including those from diverse backgrounds and technical disciplines, interested in graduate programs to ultimately provide a quality research and development workforce to both academia and industry.

Over the initial two years, a cohort of 8 diverse students each year learned fundamental self-driving and networking skills including coding for drive-by-wire vehicles, computer vision, use of localization, and interpretation of richer sensor data, as well as network and communication protocols. The students were introduced to research ideation and publishing concepts, mentored in designing and testing hypotheses, and then involved in two challenges related to self-driving and networked vehicles. Two teams of 4 designed, implemented, tested various self-drive and V2X algorithms using real vehicles on a test course, analyzed/evaluated test results, wrote technical reports, and delivered presentations. After the summer program was over, the technical reports were published in peer reviewed conferences and journals.

Survey results show that students attained significant & real-world computer science skills in autonomous vehicle development leveraging real vehicles available. The programs also increased research career interests and strengthened students’ confidence, self-guided capabilities, and research skills, while additionally supporting the development of workshop materials, simulators, and related content that provide valuable resources for others planning to develop an undergraduate curriculum to teach self-drive and networked vehicle development.

Citation
Format

Chung, C., & Siegel, J. E., & Wilson, M. (2024, March), Undergraduate Research Experiences for Automated and Connected Vehicle Algorithm Development using Real Vehicles Paper presented at 2024 ASEE North Central Section Conference, Kalamazoo, Michigan. 10.18260/1-2--45645

TY  - CPAPER
AB  - We are experiencing a revolution in vehicle operation, with fully automated robotaxis deployed and available for public use in major U.S. markets in 2023. These vehicles, while imperfect, already are arguably safer than the average human driver. Despite this rapid progress, there remain significant research and development problems that must be addressed; beyond this, there is an underdeveloped workforce for skilled researchers, developers, and practitioners in these areas, a fact that may delay necessary advances.  We have created and run for two years a National Science Foundation funded Research Experience for Undergraduates (NSF REU) focused on solving both unmet research needs, and workforce development and pipeline programs. In our REU, which makes use of simulation and two full-scale, street-legal drive-by-wire electric vehicles with perception, planning, and control capabilities, our primary goals include to (1) provide hands-on experiences to 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 and evaluation using real vehicles on real test courses, (3) strengthen their confidence, self-guided capabilities, and research skills, and (4) increase the number of students, including those from diverse backgrounds and technical disciplines, interested in graduate programs to ultimately provide a quality research and development workforce to both academia and industry.

Over the initial two years, a cohort of 8 diverse students each year learned fundamental self-driving and networking skills including coding for drive-by-wire vehicles, computer vision, use of localization, and interpretation of richer sensor data, as well as network and communication protocols. The students were introduced to research ideation and publishing concepts, mentored in designing and testing hypotheses, and then involved in two challenges related to self-driving and networked vehicles. Two teams of 4 designed, implemented, tested various self-drive and V2X algorithms using real vehicles on a test course, analyzed/evaluated test results, wrote technical reports, and delivered presentations. After the summer program was over, the technical reports were published in peer reviewed conferences and journals.

Survey results show that students attained significant &amp;amp; real-world computer science skills in autonomous vehicle development leveraging real vehicles available. The programs also increased research career interests and strengthened students’ confidence, self-guided capabilities, and research skills, while additionally supporting the development of workshop materials, simulators, and related content that provide valuable resources for others planning  to develop an undergraduate curriculum to teach self-drive and networked vehicle development.
AU  - Chan-Jin Chung
AU  - Joshua E Siegel
AU  - Mark Wilson
CY  - Kalamazoo, Michigan
DA  - 2024/03/22
PB  - ASEE Conferences
TI  - Undergraduate Research Experiences for Automated and Connected  Vehicle Algorithm Development using Real Vehicles 
UR  - https://strategy.asee.org/45645
DO  - 10.18260/1-2--45645
ER  -