Advanced Road Lane Line Detection for Autonomous Driving: Enhancing Accuracy and Robustness

Tyagi, Kapil (2023) Advanced Road Lane Line Detection for Autonomous Driving: Enhancing Accuracy and Robustness. Masters thesis, Dublin, National College of Ireland.

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Abstract

The booming advance in tech for automated cars calls for solid street lane identification to boost trustworthiness and precision. This investigation dives into creating a unique Advanced Street Lane Identifier to handle the challenge of faint lane lines and complicated road situations. Realizing the critical need for spot on lane spotting to ensure effective and safe operations of self driving cars is what fuels this study. The quest for novel tactics is powered by the lack of reliable outcomes in actual day to day scenarios.

This study uses advanced techniques like Hough transformation, Canny edge detection, and Gaussian blurring. All these methods are put together in a whole system. The truthfulness of finding lines on the road has gotten a lot better, the test results show this. Even when things get tough, it still works! In theory, with new things happening in computer sight and pictures getting changed, our job keeps up with the times. In real life, the biggest win is that cars that drive themselves can better see the lanes on the road. While the study gives hopeful results, there are still hurdles to cross before cities can effortlessly use it. These hurdles include the system's knack to adjust to changing scenarios and exploring problems during actual application. This work charts the course for the creation of more sophisticated and dependable self driving systems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Uncontrolled Keywords:	Lane detection; Autonomous driving; Conventional Neural Networks (CNNs); Road safety
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	23 May 2025 14:39
Last Modified:	23 May 2025 14:39
URI:	https://norma.ncirl.ie/id/eprint/7627

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