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Self driving car in a virtual world using Machine Learning (1)

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Self driving car in a
virtual world using
Machine Learning
Team Members: S.Ajay Kowshik , S.Dhinesh Kumar
Mentor: Mrs. .Latha Selvi , HOD
College Name: St.Joseph’s College of Engineering
Department: Information Technology
Objective
●
To create a self driving car prototype.
Motivation behind this project
●
●
Nowadays there are many people that would like their car to drive
on its own.
The advancement in technology of cars and machine learning
algorithms is the motivation behind this project.
Abstract
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. Google, the biggest network
has started working on the self-driving cars since 2010 and still developing
new changes to give a whole new level to the automated vehicles. In this
project we have focused on creating a automated car in a virtual world with
the help of Udacity. The prototype analyzes the virtual world and drives safely
without encountering any obstacles.
Literature Survey
S.No
1.
2.
Name
Design and implementation of
self-driving car - August 2020
Fathy, M., Ashraf, N., Ismail,
O., Fouad, S., Shaheen, L., &
Hamdy, A. Procedia Computer
Science
Lane detection technique based on
perspective transformation and
histogram analysis for self-driving
cars -May 2020 Muthalagu, R.,
Bolimera, A., & Kalaichelvi, V.
Computers & Electrical
Engineering
Methodology
Road lane detection algorithm, disparity
map algorithm , Anomalies detection using
Support Vector Machine classification
algorithm
Image Processing
Literature Survey
S.No
3.
4.
Name
Author
How will self-driving vehicles affect U.S.
megaregion traffic? The case of the Texas
Triangle- December 2020
Huang, Y., Kockelman, K. M., &
Quarles, N.
Deep learning for object detection and
scene perception in self-driving cars:
Survey, challenges, and open issues February 2021
Gupta, A., Anpalagan, A., Guan,
L., & Khwaja, A. SGupta, A.,
Anpalagan, A., Guan, L., &
Khwaja, A. S
Proposed System
●
Benefits of this project
● Will greatly help game developers to
implement realistic cars in their
projects.
● Act as a stepping stone for self
driving cars in the real world.
References
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Vision, 56–111. doi:10.1007/978-1-4899- 3216-7_4
2. Deepak Kadam, Prathamesh Chavan, Prashant Pandhara, “Literature Survey on Recognition and Evaluation
of Optical Character Recognition (OCR)”, International Journal of Scientific & Engineering Research Volume 9,
Issue 2, February-2018 ISSN 2229-5518
3. Dr.Jangala. Sasi Kiran1 , N. Vijaya Kumar 2 , N. Sashi Prabha 3 , M., “A Literature Survey on Digital Image
Processing Techniques in Character Recognition of Indian Languages.”, (IJCSIT) International Journal of
Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2065-2069, ISSN:0975-9646
4. S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017
International Conference on Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1-6, doi:
10.1109/ICEngTechnol.2017.8308186.
5.N.Sudhakar Reddy, M.V.Sumanth, S.Suresh Babu, "A Counterpart Approach to Attendance and Feedback
System using Machine Learning Techniques", Journal of Emerging Technologies and Innovative Research
(JETIR), Volume 5, Issue 12, Dec 2018.
5. Software testing including black box testing , white box testing and grey box testing and unit testing:
https://en.wikipedia.org/wiki/Software_testing
6.L.R Medsker, L.C Jain, “Recurrent Neural Network: Design and Application”, CRC Press International
series on Computational Intelligence, 2011.
7.Max Jaderberg, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Deep structured output
learning for unconstrained text recognition. arXiv preprint arXiv:1412.5903 (2014).
8.Baoguang Shi, Xiang Bai, and Cong Yao. 2016. An end-to-end trainable neural network for image-based
sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis
and machine intelligence 39, 11 (2016), 2298–2304.
9. Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006. Connectionist
temporal classification: labelling unsegmented sequence data with recurrent neural networks. In
Proceedings of the 23rd international conference on Machine learning. ACM, 369–376.
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