Graduation Semester and Year
Spring 2026
Language
English
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Electrical Engineering
First Advisor
Dr. H Eric Tseng
Second Advisor
Dr. Yan Wan
Third Advisor
Dr. Ahmet Taha Koru
Abstract
Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane detection models that operate reliably under adverse lighting, alongside an efficient object detection pipeline strictly optimized for autonomous driving. Vehicle control is governed by a novel hierarchical architecture that combines adaptive MPC SAC for high level steering with reinforcement learning optimized PID for low level actuation. Finally, the entire pipeline is unified by robust path planning and optimized ROS middleware, resulting in a highly capable resource efficient platform for autonomous driving research.
Keywords
Autonomous Driving Pipeline, AI-Assisted Data Annotation, Multi-Camera Calibration, Object Detection, Lane Detection, Hierarchical Control Architecture, ROS Middleware Optimization
Disciplines
Computer and Systems Architecture | Controls and Control Theory | Electrical and Computer Engineering | Navigation, Guidance, Control, and Dynamics | Robotics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Panchal, Rikkin Pankaj, "End-to-End Development and Experimental Validation of a 1/10-Scale Autonomous Vehicle" (2026). Electrical Engineering Theses. 3.
https://mavmatrix.uta.edu/electricaleng_theses2/3
Included in
Computer and Systems Architecture Commons, Controls and Control Theory Commons, Navigation, Guidance, Control, and Dynamics Commons, Robotics Commons