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Rajguru, Aishwarya Rajesh (2024) Reinforcement Learning Modelling for Autonomous Vehicle Navigation. Masters thesis, Dublin, National College of Ireland.
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Abstract
The research aimed at providing a detailed investigation of the application deep learning approaches for self-driving car navigation with particular emphasis on learning of steer angle from images. The project incorporated the Udacity self-driving car simulator, which is a robust method of image data gathering and performing of the models’ validation. Three CNN architectures were crafted and trained to improve the prediction of the steering angle. The performances of the models were assessed with metrics like Mean Squared Error (MSE) as well as the R² score, where the enhanced models evidenced great enhancements with regard to the variation in driving conditions. Three CNN models for the autonomous vehicle navigation were developed and their performance assessed. The Extended Neural Network resulted in Mean Squared Error (MSE) 0. 053 with, an R² score of -0. 12, the Deep Neural Network model poses mean squared error equals to 0. 050 and the R² of -0. 16 as compared to the other developed CNNs showing the least performance with an MSE of 0. D=071,and, R² of -0. 50.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mulwa, Catherine UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Motor Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 25 Aug 2025 10:27 |
| Last Modified: | 25 Aug 2025 10:27 |
| URI: | https://norma.ncirl.ie/id/eprint/8616 |
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