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.