Alam, Md. AshrafulArefin, Kazi RidwanHuque, Md. MashrukulTasin, Sheikh SadafKhan, Ahmed JawadAbrar, Aareef2018-05-102018-05-1020182018-04ID 13101212ID 13101232ID 13321074ID 14101258ID 17301238http://hdl.handle.net/10361/10123Cataloged from PDF version of thesis.Includes bibliographical references (pages 41-42).This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.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.42 pagesenBRAC 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.AutonomousSelf-driving carNVIDIA modelMax-poolingThesis