CAT Vehicle

Program Overview

A 10-week research for undergraduates (REU) program on autonomous ground vehicles, controls, communications, vehicular security, machine learning, and algorithms. Students will work side-by-side with faculty and graduate research assistants who are experts in these areas.

Program Highlights

This REU will support 10 students. Each student will receive:
Up to $600 travel allowance
$6,000 stipend
On-campus housing
Letters of Recommendation from faculty mentors

Apply Now! Application Step 1

Start your application process here. We use the CISE REU Common Application, which allows you to be considered for other REU Site opportunities (if you elect to do so).

Baschab, Ball, and Vazanna publish at CPS-IoT Week

CPS-IoT Week is the premier event on Cyber-Physical Systems and Internet-of-Things Research, and three researchers from the CAT Vehicle will have their results published there in 2021. Emily Baschab (University of Alabama), Savannah Ball (Monmouth College), and Audrey Vazzana (Rose-Hulman Institute of Technology) continued work on their final paper, and submitted as a Work-In-Progress to the conference, where they will have an opportunity to present a poster on their work.

L-R: Audrey Vazzana, Savannah Ball, and Emily Baschab

Kreienkamp, Fishbein, Publish at American Control Conference

“Although I wish the conference could have been in person so I could have met other researchers in Denver, I find myself very lucky to have worked with my team, and to have been able to participate in the CAT Vehicle REU program," said Christopher Kreienkamp, whose presentation was through a YouTube video due to the COVID-19 policies of the conference. Kreienkamp and his partner Daniel Fishbein, were part of CAT Vehicle 2019.

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Apply

Apply Now! Application Step 1

Start your application process here. We use the CISE REU Common Application, which allows you to be considered for other REU Site opportunities (if you elect to do so).

Application Step 2: Additional Materials

This is Part 2 of the application process: you must also fill out the CISE REU Common Application (Part 1), if you have not done so already. You can revisit this page at any time to upload your application materials, but please only fill out the form one time. Please make sure you press "Upload" after specifying the two files in the form below, prior to pressing the "Submit" button.

Application Part 2 Submission: https://forms.gle/VxEbSynu6vdsnF5W7. Note that a Google account is required due to the file upload.

Important Dates

January 2023: Applications open
19 March 2023: Applications due, to guarantee full consideration.
20-27 March 2023: Letters of Recommendation due, to guarantee full consideration
28 March -7 April 2023: Selection process
10 April 2023: Notification deadline
June-August 2023: 10-week program

Important Information

This REU site will support 10 students over the summer. Each student will receive:

Stipend of $6,000 over the summer
Housing and $600 travel allowance to Tucson, AZ
Letters of recommendation from their faculty mentors

Students will participate as researchers for the summer, working side by side with graduate researchers and faculty who are experts in cognitive radio and autonomous ground vehicles. Want to know more about what the REU is like? Check out the videos made by previous CAT Vehicle participants.

REU 2018: Lane detectives showing their pride after successful testing

Reference Letter

Are you a faculty member submitting a letter on behalf of an applicant? We will begin reviewing applications one week after the application deadline.

CAT Vehicle 2020

Real-Time Traffic Light Identification using YOLOv3 Algorithm For Autonomous Vehicles

Rachel Kozel (Purdue University)
Naeemah Robert (New York Institute of Technology)

Previous methods to detect traffic signals were not fully successful as they had limitations in detecting yellow and arrow traffic lights, in long-distance identification of a traffic light from a few pixels, and the obstruction of traffic lights from other infrastructure. This project uses a new method to identify traffic lights and their states in both urban and suburban areas by developing a deep learning model utilizing a You Only Look Once (YOLOv3) model.

Generative modeling of driving profiles

Megan Walter (University of Oregon)
Iris Jones (Washington State University)

The validity of simulation testing for autonomous vehicles depends on the ability to accurately simulate human driving behavior. This project seeks to train a model on an individual's driving data, and to test the ability of the model to predict trajectories that replicate the driver's style by using the model in a realistic simulated environment.

Hyperparameter Optimization using Grid Search for use in Monocular Depth Estimation

Calvin Barrett (Haverford College)
Tomo Bessho (University of Nebraska-Lincoln)

While an autonomous vehicle operates, it utilizes a myriad of sensors to observe its surroundings, including images that are used to train and optimize neural networks. This project explores how to set parameters for machine learning for detecting objects in these images.

Safer adaptive cruise control for traffic wave dampening

Audrey Vazzana (Rose-Hulman Institute of Technology)
Savannah Ball (Monmouth College)
Emily Baschab (University of Alabama)

Current adaptive cruise controllers can use radar sensors to follow a vehicle ahead at high speeds (40+ mph), but reach their limits if the vehicle ahead must slow down or stop, requiring the driver to resume control over the car’s speed. This project explores how to create low-speed adaptive cruise controllers that can safely operate in congested traffic.

(L) Audrey Vazzana; (C) Savannah Ball; (R) Emily Baschab