Study on Hybrid Autonomous Trucking System (HATS) Problem Complexity

  • Jackson Brunetto Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • John Froboese Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • Edward Hieb Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • Anthony Ikeogu Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • Freddy Lema Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • Cesar Rocha Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
  • Matthew Rowe Texas Tech University, Mechanical Engineering Department, Lubbock, Texas, 79409, USA
DOI: https://doi.org/10.22545/2018/00107
Keywords: electric hybrid, autonomous, transdisciplinary approach, artificial intelligence, trucks

Abstract

The goal of this study is to break down and analyze the complexities surrounding the implementation of an electric hybrid autonomously controlled tracking system (HATS) for the mass transportation of goods. HATS represents a new category of commercial vehicles that exhibit both electric hybrid powertrains, and artificial intelligence for self-driving capabilities. Problem complexities are broken down from a social, economic and environmental perspective using a transdisciplinary approach. Study motivation arises from the fact that tons of goods are moved thousands of miles across the world using semi-trucks and in order to do so, thousands of drivers spend days on end out on the open road. This results in high CO2 emissions as well as loss of precious family time for drivers. As a potential solution, modern computing technology offers artificial intelligence (AI) to increase efficiency and replace these drivers all together. Considering this potential however, there are a significant number of issues surrounding the proposition that need to be solved before implementation can begin.

Published
2018-01-01
How to Cite
Brunetto, J., Froboese, J., Hieb, E., Ikeogu, A., Lema, F., Rocha, C., & Rowe, M. (2018). Study on Hybrid Autonomous Trucking System (HATS) Problem Complexity. Transdisciplinary Journal of Engineering & Science, 9. https://doi.org/10.22545/2018/00107
Issue
Vol 9 (2018)
Section
Articles