Analysis of training time optimization for self-driving car using alternate max pooling layers
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Lingua: | English |
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BRAC University
2018
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| Accesso online: | http://hdl.handle.net/10361/10123 |
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10361-10123 |
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10361-101232022-01-26T10:15:50Z Analysis of training time optimization for self-driving car using alternate max pooling layers Arefin, Kazi Ridwan Huque, Md. Mashrukul Tasin, Sheikh Sadaf Khan, Ahmed Jawad Abrar, Aareef Alam, Md. Ashraful Department of Computer Science and Engineering, BRAC University Autonomous Self-driving car NVIDIA model Max-pooling This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-42). In the modern era, the vehicles are focused to be automated to give human driver relaxed driving. In the field of automobile various aspects have been considered which makes a vehicle automated. As Udacity, in 2016, with the Google Self-Driving Car founder Sebastian Thrun open sourced their self driving car simulation environment, a new door opened up in self driving vehicle research. In this paper we have focused on comparing between a popular neural network model introduced by NVIDIA and a model made with max pooling optimized neural network. Max pooling is a method of making Convolutional Neural Network simpler. We have also worked vigorously on creating a custom urban environment based on Dhaka, where we have run the car autonomously, gathered the training time data, and the instances it fails to drive along the trained roadway. The idea described in this paper is related to deep learning algorithm analysis and comparison, which is the core part of the self driving car. The analysis hence gives us a great realization of machine learning techniques and their effectiveness in practical situations. All being said, this paper, however approaches to solve one big problem, to find out practicality of a popular model compared to an unconventional model, put in a real scenario. Kazi Ridwan Arefin Md. Mashrukul Huque Sheikh Sadaf Tasin Ahmed Jawad Khan Aareef Abrar B. Computer Science and Engineering 2018-05-10T10:39:34Z 2018-05-10T10:39:34Z 2018 2018-04 Thesis ID 13101212 ID 13101232 ID 13321074 ID 14101258 ID 17301238 http://hdl.handle.net/10361/10123 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 42 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Autonomous Self-driving car NVIDIA model Max-pooling |
| spellingShingle |
Autonomous Self-driving car NVIDIA model Max-pooling Arefin, Kazi Ridwan Huque, Md. Mashrukul Tasin, Sheikh Sadaf Khan, Ahmed Jawad Abrar, Aareef Analysis of training time optimization for self-driving car using alternate max pooling layers |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Arefin, Kazi Ridwan Huque, Md. Mashrukul Tasin, Sheikh Sadaf Khan, Ahmed Jawad Abrar, Aareef |
| format |
Thesis |
| author |
Arefin, Kazi Ridwan Huque, Md. Mashrukul Tasin, Sheikh Sadaf Khan, Ahmed Jawad Abrar, Aareef |
| author_sort |
Arefin, Kazi Ridwan |
| title |
Analysis of training time optimization for self-driving car using alternate max pooling layers |
| title_short |
Analysis of training time optimization for self-driving car using alternate max pooling layers |
| title_full |
Analysis of training time optimization for self-driving car using alternate max pooling layers |
| title_fullStr |
Analysis of training time optimization for self-driving car using alternate max pooling layers |
| title_full_unstemmed |
Analysis of training time optimization for self-driving car using alternate max pooling layers |
| title_sort |
analysis of training time optimization for self-driving car using alternate max pooling layers |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10123 |
| work_keys_str_mv |
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| _version_ |
1814308364069371904 |