The summer of 2020 provided many challenges. For 9 undergraduate students across the United States, the summer also provided a research experience in autonomous driving and its many applications. In spite of the many challenges posed by the pandemic with respect to travel, networking, and person-to-person meetings, these undergraduate researchers showed tremendous resilience and perseverance in working on projects remotely and communicating largely through video conferencing and workspace text apps.
For more information on the topics and contributions of the four teams of researchers, please visit http://catvehicle.arizona.edu/cat-vehicle-2020 for a listing of their projects, and also links to individual videos they produced about their research experience.
“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.
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.
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.
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.
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.
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.