Human-Centric Smart Cities: A Digital Twin-Oriented Design of Interactive Autonomous Vehicles

Author

Oscar G. De Leon-Vazquez, The University of Texas Rio Grande Valley

Date of Award

12-2023

Document Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Mechanical Engineering

First Advisor

Fatemeh Nazari

Second Advisor

Constatine Tarawneh

Third Advisor

Mohamadhossein Noruzoliaee

Abstract

Autonomous vehicle (AV) technology is introduced as a solution to improve transportation safety by eliminating traffic accidents caused by human error, which is the leading cause of 90% of accidents. One key feature of AVs is sensing and perceiving their surrounding environment through processing observations collected from the environment. The perception system is essential for an AV to make informed decisions and safely navigate the environment. This study presents an image semantic segmentation algorithm developed in the area of computer vision to improve AV perception. The U-Net-based algorithm is trained and validated using a synthetically generated dataset in a simulation environment, namely, CAR Learning to Act (CARLA). The results indicate an improved accuracy of up to 98% compared to the existing methods. The performance of the proposed model is further analyzed using various evaluation metrics.

Comments

Copyright 2023 Oscar G. De Leon-Vazquez. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/pqdtglobal1/dissertations-theses/human-centric-smart-cities-digital-twin-oriented/docview/2928508511/sem-2?accountid=7119

Recommended Citation

De Leon-Vazquez, Oscar G., "Human-Centric Smart Cities: A Digital Twin-Oriented Design of Interactive Autonomous Vehicles" (2023). Theses and Dissertations. 1440.
https://scholarworks.utrgv.edu/etd/1440