Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science in Engineering (MSE)


Mechanical Engineering

First Advisor

Fatemeh Nazari

Second Advisor

Constatine Tarawneh

Third Advisor

Mohamadhossein Noruzoliaee


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.


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