We want your application! You can start the application process now, and assemble your final materials later. If you start your application process early, we can remind you as the deadline approaches. The application process involves:

  1. Filling out the application with readily available information (GPA, courses taken, address, etc.)
  2. Visiting the site later to submit additional materials (statement of purpose, unofficial transcript, names of letter writers, etc.)
  3. Reminding your letter writers to submit their recommendations.

CAT Vehicle REU 2018 Team with  the self-driving car CAT Vehicle

Students from all over the US gathered at the University of Arizona to work on 10 weeks long summer program to bring the self-driving CAT Vehicle in action. They demonstrated their algorithms with the CAT Vehicle on Tuesday August 9, 2016 early morning on West side of the ECE Building. The ECE building is located near the southwest corner of Speedway and Mountain.

CAT Vehicle self-driving car. Photo: University of Arizona

Sterling Holcomb, Audrey Knowlton, and Juan Guerra published results of their research project in WinnComm'16, the Wireless Innovation Forum Conference on Wireless Communications Technologies and Software Defined Radio, in Reston, VA. Their paper, titled "Power Efficient Vehicular Ad Hoc Networks" was a new approach for Vehicular AdHoc Networks (VANETS) that represents a significant reduction in power use. This benefit enables more vehicles to communicate simultaneously.

Yegeta Zeleke and Kennon McKeever published results of their research project in the 15th Workshop on Domain-Specific Modeling, which is the longest-running workshop in the history of SPLASH/OOPSLA.

This research experience for undergraduates (REU) is engaged in the myriad of applications that are related to autonomous ground vehicles. This summer, 10 NSF-funded undergraduate students participated in an immersive research experience, sitting side-by-side with graduate researchers and working on one of the most compelling, and complex, applications of today: autonomous systems.

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We want your application! You can start the application process now, and assemble your final materials later. If you start your application process early, we can remind you as the deadline approaches. The application process involves:

  1. Filling out the application with readily available information (GPA, courses taken, address, etc.)
  2. Visiting the site later to submit additional materials (statement of purpose, unofficial transcript, names of letter writers, etc.)
  3. Reminding your letter writers to submit their recommendations.

CAT Vehicle REU 2018 Team with  the self-driving car CAT Vehicle

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).

This is Part 2 of the application process: you must also fill out the CISE REU Common online application form for Arizona, 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.

Update: If the form fails to submit and provides the error message(s) "Statement of Purpose Field is Required" and/or "Transcript Field is Required", then add your files but do not press the upload button. This is a documented problem with the latest version of our web server.

  • 1 March: Applications due, to guarantee full consideration
  • 8 March: Letters of Recommendation due, to guarantee full consideration
  • 9-23 March: Selection process
  • 1 April: Notification deadline
  • April-May: Preparation and background reading
  • June-August: 10-week program.

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

  • Stipend of $5,000 over the summer
  • Housing, meal allowance, 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

CAT Vehicle 2018

Ashley Aponik (Yale University)
William Robert Anderson (Wofford College)
Youssef T Daoud (Tennessee Tech University)

The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a primary user and secondary user are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including ε-greedy, Softmax Strategy, and Q-Learning

Samantha Harris (Stetson University)
Levi Welch (University of Michigan at Flint)

In this research project, students used verification methods to produce safe code for the CAT Vehicle, the autonomous vehicle being developed at the University of Arizona. The verification methods ensure that the network of the autonomous vehicle runs within four constraints. The four constraints are cost, processing power, bandwidth, and latency. Operating within these constraints allows the car to maximize its data processing potential.

Hannah Grace Mason (Lipscomb University)
Joe MacInnes (The College of Wooster)
Landon Bentley (The University of Alabama)

In this project, students developed a comprehensive lane-detection and lane-following system for autonomous vehicles. Their system was tested using hardware-in-the loop simulation and used sensors such as stereocamera, GPS and gyroscope from a mobile device.

CAT Vehicle 2017

Khalil Anderson (University of Maryland, Baltimore County)
Marti Hands (Texas Tech University)
Lauren Lusk (University of Oklahoma)

In this project, students measured the occupancy of the TV white space and performed simulation to show how a network using this band performs under the multiple scenario of everyday driving.

Christopher Alicea-Nieves (Interamerican University of Puerto Rico, Bayamon Campus)
Kaitlyn Oura (University of Arizona)
William Silloway (Kennesaw State University)

In this project, students created a high-level domain-specific modeling language for non-experts to design behaviors and run them on an automated vehicle.

Niamke Giraud (University of Massachusetts, Dartmouth)
George Gunter (University of Illinois at Urbana-Champaign)
Yuriy Slashchev (Stony Brook University)

This project aims to detect and track other vehicles with increased precision using LIDAR and create a better model of traffic flow.