The intellectual focus of the program includes the following research topics:

  1. Model-based design for component-based systems;
  2. Wireless networks;
  3. Cognitive radio;
  4. Embedded control systems and algorithms;
  5. Sensory data processing and sensor fusion;
  6. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication; and
  7. Scalability of algorithms/systems (multi-vehicle, multi-device).

Students need not have existing expertise in these areas, a background sufficient for standing as an engineering or computer science/mathematics senior will be sufficient. Additional background and cross-cutting themes in these topics will be covered in short courses that are presented by the graduate student mentors, and selected faculty from the ECE department, in order to address common tool usage questions, and familiarity with fundamental concepts.

Applicants must choose either "Autonomy" or "Cognitive Radio" when submitting their application. This choice allows us to ensure that the program will contribute to both areas. More information on this choice can be found on the application page.

CAT Vehicle 2019

This video describes my experience with the CAT Vehicel REU program at the University of Arizona during the 2019 summer. During this program, I realized I enjoy making games for reinforcement learning and what I wanted to research in graduate school. I would recommend this program due to the great mentors who supported me and the friends I made over the summer.

Video about Chris's CAT Vehicle Project: showing that a vehicle with FollowerStopper in a singular lane without merges will not crash and that it will be string stable, effectively dissipating human-caused traffic waves if enough vehicles are deployed in the traffic flow.

Video of Daniels project: checking whether the velocity controller, FollowerStopper, is safe and string stable. 

This silly video provides a brief overview of what our group (Jill Alexander and I) accomplished during our time at the UofA CAT Vehicle REU.

Video by 2019 CAT Vehicle Alum Jill Alexander.