Graduation Semester and Year
Spring 2025
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Diego Patino
Second Advisor
Marnim Galib
Third Advisor
Alex Dillhoff
Abstract
In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras operate on a fundamentally different paradigm, registering only pixel-level brightness changes with microsecond precision. This asynchronous sensing approach offers intriguing advantages—high dynamic range exceeding 140 dB, negligible motion blur, and exceptional temporal resolution—that may prove transformative for safety-critical applications in dynamic environments. Our research framework systematically investigates the application of Histogram of Gradients (HOG) feature extraction to event data streams, exploring how these structured representations can be leveraged by contemporary deep learning architectures including transformer-based models and attention mechanisms. Through careful experimental design utilizing the Microsoft AirSim simulation environment and the TuSimple dataset, we examine the comparative performance of various architectural approaches when processing event-based inputs. The central hypothesis guiding this work posits that the integration of event-based vision with tailored feature extraction might transcend the limitations of traditional frame-based methods, particularly in scenarios involving poor visibility, dynamic lighting, or rapid motion. By developing and evaluating a comprehensive pipeline for event-based lane detection, this thesis aims to contribute meaningful insights to both the theoretical understanding and practical implementation of more resilient autonomous driving systems.
Keywords
Lane detection, Event-based cameras, Histogram of Gradients (HOG), Deep learning architectures, Vision Transformers, Autonomous driving
Disciplines
Computer and Systems Architecture | Other Computer Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Gupta, Ganesh, "Event-Based Histogram of Gradients for Lane Detection" (2025). Computer Science and Engineering Theses. 527.
https://mavmatrix.uta.edu/cse_theses/527