Date of Award
Master of Science in Engineering
Mechanical and Aerospace Engineering
Zachary D. Asher, Ph.D.
Richard T. Meyer, Ph.D.
Damon A. Miller, Ph.D.
Autonomous vehicles, heavy-duty vehicles, high-definition maps, modeling, operational design domain, simulation
Masters Thesis-Open Access
Modern automobiles have greatly advanced in recent years, with technological developments that enhance performance, safety, and comfort. However, there is still much room for improvement. Today’s vehicles are heavily reliant on the combustion of fossil fuels, proven to be harmful for the environment on both a local and global scale. In addition, the safety benefits of autonomous vehicles and advanced driver assistance systems are not yet fully realized due to the limited operational design domain of these technologies. In this research, these needs are addressed through the development of two novel data pipelines. In the first study, a novel methodology is outlined that generates vehicle fuel economy models from real-world sparse fleet telematics data, enabling heavy-duty vehicle fleet operators to explore the minimization of fueling costs in new ways and to serve as an initial probe into a fully data-informed pipeline. In the second study, a data processing procedure is described that recovers lane-line geometry information from high-definition map data, expanding the operational design domain of automated vehicle technology to scenarios where lane lines are totally occluded by snow, leaves, or shadows. It is concluded that these methodologies have significant potential to improve the fuel efficiency and operational design domain of future vehicles.
Carow, Kyle James, "Improving Future Vehicle Fuel Economy and Operational Design Domain Through Novel Data Pipelines" (2023). Masters Theses. 5366.