Affordable Autonomous Vehicles for Deployment after Disastrous Events

Author

Shannon Abolmaali

Graduation Semester and Year

2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Erick Sr. Jones

Abstract

In disastrous events such as hurricanes and tornadoes, it has been observed that people get stranded and helpless without a feasible way to escape during those emergency situations. This became very evident during hurricanes, such as Katrina and Ida affecting millions of people seeking immediate rescue efforts. With the use of artificial intelligence and machine learning, we envision an autonomous vehicle, AV, which is able to find the most optimal and safest way to help those who are stranded to get them to a safe location. Electric vehicles, EV, and Autonomous Vehicles, AV, is becoming the future; minimizing the carbon footprint, reducing accidents, and revolutionizing the car industry with new car production. The challenge for the next generation is the affordability of these new vehicles and their intelligence. Technology has been advancing making these vehicles autonomous from a level 0 (no automation) to level 5 (full driving automation), but cost is a significant factor making these vehicles very expensive. This research seeks to leverage affordable sensors, artificial intelligence, and machine learning to develop a smart car kit that can be retrofitted to any type of vehicle to make it smart. The novelty of this research is that we are utilizing less expensive technologies, such as, cameras, proximity sensors, and RFID technologies and also that this kit can be used to make older vehicle models smart. The future of work components of this research is the autonomous learning to minimize human risk in disaster recovery.

Keywords

RFID, Artificial intelligence, Autonomous vehicles, Machine learning

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Comments

Degree granted by The University of Texas at Arlington

Recommended Citation

Abolmaali, Shannon, "Affordable Autonomous Vehicles for Deployment after Disastrous Events" (2022). Industrial, Manufacturing, and Systems Engineering Dissertations. 177.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/177