Abstract
My technical report details a spacecraft design project where we address Virginia
transportation issues using remote sensing and data fusion methods. The project requires
gathering data on real time weather forecasts and traffic patterns to provide more accurate
information on weather and road conditions to drivers, autonomous vehicles, and satellite
navigation services. Although frequent data collection is imperative to the success of this project,
it is just as important to consider the negative consequences of excessive data collection. Over
the past decade, big data has allowed for advancements in industries such as healthcare, banking,
retail, and more. Despite these advancements, big data also warrants the need for regulations
concerning consumer privacy and safety. The STS research paper explores ideas of excessive
data collection and “dark data.”
This academic year, my UVA spacecraft design capstone class worked closely with
MITRE to use real time weather data to improve Virginia’s roadway safety. Although roadway
users may rely on weather forecasts, the Virginia Department of Transportation uses road
condition measurements that differ from meteorological data reported to drivers. There is
currently no transparent method to provide this information to drivers. Our primary mission
objectives were to detect and identify snow-covered, ice-covered, or dry highways using remote
sensing techniques and effectively distribute this data to roadway users and managers to improve
safety using a 6U CubeSat spacecraft. A full conceptual design review detailing mission
objectives and constraints, spacecraft subsystems, preliminary designs, financial budget, and risk
mitigation strategies has been developed and incorporated in the technical report.
The STS research paper explores the implications of data-driven technologies through a
framework known as Value Sensitive Design (VSD). VSD is a design methodology that advocates for the integration of human values when planning or designing a new technology.
User values such as privacy and autonomy are often not considered during the design phase of a
new technology, leading to privacy concerns among the public. The research paper conducts a
conceptual, empirical, and technical investigation of data-driven technologies and identifies
where the value discrepancies between corporations and consumers may lie. Furthermore, the
paper explores the idea of dark data, information that organizations may not be able to see or do
not know has been collected, and answers how corporations can limit the amount of dark data
collected.
The work done so far in the spacecraft design capstone has the potential to improve
roadway conditions and prevent weather-related traffic accidents in Virginia, and potentially the
United States. While working on this project, I not only refined technical skills in CubeSat
design, but learned how to effectively tackle a large problem and communicate with stakeholders
through the space mission engineering process. Furthermore, the work I have done in my STS
research will ensure that I keep human values in mind while designing technologies in my career
in the future. Lastly, I would like to thank Professor Chris Goyne for his guidance in the
spacecraft design capstone course and Professor Sean Ferguson for all his feedback during my
STS research.
Notes
School of Engineering and Applied Science
Bachelor of Science in Aerospace Engineering
Technical Advisor: Chris Goyne
STS Advisor: Sean Ferguson
Technical Team Members: Arianna Asquini, Isaac Burkhalter, Xavier Castillo-Vieria, Mici Cummings, Andrew Curtin, Andrianna Daniels, Ian Davis, Luke Dennis, Cooper Dzema, Kyle Ebanks, Shane Eilers, Graham Fitzgerald, Kevin Fletcher, Rikia Freeman, Raeann Giannattasio, Brandon Ghany, Jalen Granville, Alex Griffin, Allen Lang, Dorothea LeBeau, Dominic Pinnisi, Colin Purcell, Bailey Roe, Khamal-Karim Saunders, Anisha Sharma, Jimmy Smith, Pranav Sridhar, Elias Topp, Nana-Ayana Tyree, Ethan Vicario, Avery Walker, Ian Wnorowski, Victor Yang
