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 1. Sonka, M., Hlavac, V., & Boyle, R. (1993). Image pre-processing. Image Processing, Analysis and Machine 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.