This REU site, in collaboration with Michigan State University, provides hands-on active learning and research opportunities for undergraduate students to develop, test, analyze, and evaluate self-drive and V2X algorithms on street legal vehicles. The results will be published in posters and papers.
Application Process
Applications with your personal statement, resumé, transcripts, and two letters of recommendation through NSF ETAP at: etap.nsf.gov
Accepted students will be notified on a rolling basis until all positions filled or by April 18, 2023. Late applications may be considered if funding remains available.
For more information you can contact Dr. CJ Chung, Professor, Math & Computer Science Department, College of Arts & Sciences at cchung@ltu.edu.
Accepted participants receive a total of $6,080 (paid biweekly) and free housing in LTU’s dorms. Local students may choose to commute; students outside Metro Detroit will be eligible for travel reimbursement up to $500.
To be eligible to apply for this program all applicants must:
- be U.S. citizens or hold permanent residency status.
- have a cumulative GPA of at least 3.2 (on a 4.0 scale).
- be a college (including community college) freshman, sophomore, or junior as of the fall 2023 semester. High school graduates who have been accepted at an undergraduate institution but who have not yet started their undergraduate study may be eligible.
- major or plan to major in STEM (Science, Technology, Engineering, Mathematics).
- have a valid Driver’s license.
- have completed calculus I & II.
- have taken at least two computer science course with Python, Java, JavaScript, C, or C++. Linux and ROS (Robot Operating System) experience is preferred.
- not be enrolled in classes during Summer 2023.
- be available to work full-time (at least 40 hours per week) on the campus of LTU from May 24 - July 18, 2023 (8 weeks).
- agree to participate in occasional, brief follow-on surveys after the program completion.
Chan-Jin “CJ” Chung, PhD is a Professor of Computer Science at Lawrence Technological University. He founded the Robofest autonomous robot competition for 5-12 grade and undergraduate students. Over 30,000 students from 18 US States and 28 countries have participated in Robofest since 1999. He launched numerous STEM+CS education programs using autonomous robots such as RoboParade in 2006, Robot Fashion & Dance Show that became RoboArts in 2007, Vision Centric Robot Challenge in 2007, and MathDance (Learning Computer Science with Physical Activities and Animation) in 2017. Chung has been a faculty advisor of LTU’s IGVC (Intelligent Ground Vehicle Competition) teams since 2003. His robot soccer team consisting of LTU undergraduate students was selected to represent the USA to compete at RoboCup four-legged division in 2007. Currently, he leads research projects on self-drive software development using the two electric vehicles. He has been a PI for the US Army RTK (Robotics Tool Kit) project since 2018.
Biography
Josh Siegel, PhD is an Assistant Professor of Computer Science and Engineering at Michigan State University and the lead instructor of the Massachusetts Institute of Technology's DeepTech Bootcamp. He received Ph.D., S.M. and S.B. degrees in Mechanical Engineering from MIT. Josh and his automotive companies have been recognized with accolades including the Lemelson-MIT Student Prize and the MassIT Government Innovation Prize. He has multiple issued patents, published in top scholarly venues, and been featured in popular media. Dr. Siegel's ongoing research develops architectures for secure and efficient connectivity, applications for pervasive sensing including vehicle diagnostics, and new approaches to automated driving.
Nicholas Paul, MSCS is an adjunct faculty member teaching ROS classes at LTU. He has worked with autonomous software development with the LTU vehicles since 2017 as a member as well as a co-adviser of the LTU IGVC self-drive team. He co-authored two journal papers and seven conference papers on autonomous vehicle research using the LTU vehicles. Paul is an AI engineer at SoarTech, a spin-off from the Artificial Intelligence Laboratory of the University of Michigan, where its founding team developed TacAir-Soar from 1992-1997 under the leadership of Professor John E. Laird. He has also been involved in the US Army RTK project.
Joe DeRose, PhD has been an adjunct faculty member at LTU since 2005 teaching vehicle dynamics as well as ROS classes. Joe served as the faculty adviser of LTU’s SAE Baja team, 2008~2013, and has mentored several Mechanical Engineering senior design projects. He has been teaching robotics programming for Robofest since 2013. He has mentored undergraduate student self-drive vehicle projects that won 2nd place Best Research Poster award in the 8th annual research day, April 2021 sponsored by Howard Hughes Medical Institute. Joe is a Driver Assistance Technology (DAT) virtual verification engineering supervisor at Ford Motor Company. Joe has extensive numerical methods and simulation experience in linear and non-linear mechanics, multibody dynamics, vehicle dynamics and assisted driving. Joe also created internal technical seminars and co-authored patents during his time at Ford.
Mitchell Pleune, BSCS has 4+ years of experience working with autonomous software development with the LTU vehicles. He was the first author of three conference papers directly from autonomous vehicle research using the LTU vehicles. He is currently a software engineer at Veoneer, a worldwide leader in autotech, producing sensors, control units, software and systems for restraint control systems, advanced driving assistance systems (ADAS), and collaborative and automated driving. He has also been involved in the US Army RTK project.
External Evaluator
Mark Wilson, PhD is a Professor of Urban and Regional Planning at Michigan State University and Program Director for the PhD in Planning, Design & Construction. His research focuses on the social context and impact of technology, in particular the internet, mobile phones and autonomous mobility. Recent research projects included examination of the role of language in public perceptions of AVs, and the use of social media to understand how influencers on Twitter shape technology narratives. He participated in the development of MSUs REU on Sociomobility and served as a faculty leader/mentor for a research project with students exploring the sustainable adaptive reuse of garages resulting from widespread AV adoption. One of his courses at MSU is an interdisciplinary social science class on The Future City that considers how emerging technologies will affect urban life. Wilson is interested in the public understanding of technology through academic and popular media, and promoting student communication skills. Public engagement through The Conversation and the MSU Science Festival provide opportunities to inform wide audiences of technology and its application. As an editorial board member for the Journal of Urban Technology he has served as guest editor on several issues, including an issue on autonomous vehicles to be published this fall.
Biography
Publications
Rao S, Quezada A, Rodriguez S, Chinolla C, Chung C-J, Siegel J. Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles. Vehicles. 2022; 4(4):1012-1041.
https://doi.org/10.3390/vehicles4040055
R. Kaddis, E. Stading, A. Bhuptani, H. Song, C. -J. Chung and J. Siegel, "Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Electric Vehicles on a Test Course," 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS), 2022, pp. 687-692
https://doi.org/10.1109/MASS56207.2022.00101
Autonomous Intersection Management Systems (AIMS)
Aarna Bhuptani, Cebastian Chinolla, Ryan Kaddis, Shika Rao, Alexander Quezada, Seth Rodriguez, Heather Song, Enver Stading
Developing, Analyzing, and Evaluating Self-Drive (Lane Following) Algorithms
Team Triangle: Alexander Quezada, Seth Rodriguez, Shika Rao, Cebastian Chinolla
Final Presentation [PDF]
Team Star: Ryan Kaddis, Enver Stading, Aarna Bhuptani, Heather Song
Final Presentation [PDF]
Acknowledgements
Support for this program is provided by the National Science Foundation.
Award #CNS-2150292.
