Exploring Strategies for Adapting Traditional Vehicle Design Frameworks to Autonomous Vehicle Design
Date of Conferral
2020
Degree
Doctor of Information Technology (D.I.T.)
School
Information Systems and Technology
Advisor
Nicholas Harkiolakis
Abstract
Fully autonomous vehicles are expected to revolutionize transportation, reduce the cost of ownership, contribute to a cleaner environment, and prevent the majority of traffic accidents and related fatalities. Even though promising approaches for achieving full autonomy exist, developers and manufacturers have to overcome a multitude of challenged before these systems could find widespread adoption. This multiple case study explored the strategies some IT hardware and software developers of self-driving cars use to adapt traditional vehicle design frameworks to address consumer and regulatory requirements in autonomous vehicle designs. The population consisted of autonomous driving technology software and hardware developers who are currently working on fully autonomous driving technologies from or within the United States, regardless of their specialization. The theory of dynamic capabilities was the conceptual framework used for the study. Interviews from 7 autonomous vehicle hard and software engineers, together with 15 archival documents, provided the data points for the study. A thematic analysis was used to code and group results by themes. When looking at the results through the lens of dynamic capability theory, notable themes included regulatory uncertainty, functional safety, rapid iteration, and achieving a competitive advantage. Based on the findings of the study, implications for social change include the need for better regulatory frameworks to provide certainty, consumer education to manage expectations, and universal development standards that could integrate regulatory and design needs into a single approach.
Recommended Citation
Munoz, Alex, "Exploring Strategies for Adapting Traditional Vehicle Design Frameworks to Autonomous Vehicle Design" (2020). Walden Dissertations and Doctoral Studies. 7944.
https://scholarworks.waldenu.edu/dissertations/7944
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Mechanical Engineering Commons