Abstract

It includes Deep Learning techniques using convolutional neural networks for getting the predictions and probabilities for the best decision that has to be made while driving a car like lane detection, traffic signals recognition and their localization and simultaneously developing a steering model to help to take decision regarding steering wheel, throttle thus stimulate a car like humans. In this paper, lane detection is the main concern is to move the steering in appropriate direction with proper angle and the problem is solved using convolutional neural networks via creating a model which then is trained over some collected training data of steering decisions on a track in a simulated environment.

References

• Brilian Tafjira Nugraha, Shun-Feng Su, Fahmizal et al., “Towards Self-driving Car Using Convolutional Neural Network and Road Lane Detector”, 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT), IEEE, 2017.
• Mochamad Vicky Ghani Aziz, Ary Setijadi Prihatmanto, Hilwadi Hindersah et al., “Implementation of Lane Detection Algorithm for Self-Driving Car on Toll Road Cipularang using Python Language”, 4th International Conference on Electric Vehicular Technology (ICEVT), IEEE, 2017.
• LecunY, Bottou L, Bengio Y, et al.”Gradient-based learning applied to document recognition”, Proceedings of the IEEE, pp-2278-2324,1998
• Davida Del Testa, Daniel Dworakowski, Jiakai Zhang et at., “End to End for Self Driving Cars”, Nvidia Corporation Holmdel, NJ 07735, 2016.
• Zhang, Zhengyou et al., "Camera calibration with one-dimensional objects." IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 892-899, 2004
• Zhang, Zhengdong, Yasuyuki Matsushita, and Yi Ma. et al., “Camera calibration with lens distortion from low-rank textures." CVPR 2011. IEEE, 2011.
• Gao, Wenshuo, et al., "An improved Sobel edge detection.", IEEE 2010 3rd International Conference on Computer Science and Information Technology, Vol. 5, 2010.
• Vincent, O. Rebecca, and Olusegun Folorunso.et al., "A descriptive algorithm for sobel image edge detection.", Proceedings of Informing Science & IT Education Conference (InSITE). Vol. 40. California: Informing Science Institute, 2009.
• Nan, Liangliang, and Peter Wonka, et al., "Polyfit: Polygonal surface reconstruction from point clouds.", Proceedings of the IEEE International Conference on Computer Vision, 2017.
• Wang, Jason, and Luis Perez et al., "The effectiveness of data augmentation in image classification using deep learning." Convolutional Neural Networks Vis. Recognit , 2017.
• [Rausch, Viktor et al. "Learning a deep neural net policy for end-to-end control of autonomous vehicles.”, 2017 American Control Conference (ACC) IEEE, 2017.
• Pierre Sermanet and Yann LeCun et al., “Traffic Sign Recognition with Multi-Scale Convolutional Networks”, Courant Institute of Mathematical Sciences, New York University
• Neena Aloysius and Geetha M,” A Review on Deep Convolutional Neural Networks”, International Conference on Communication and Signal Processing(IEEE), pp-588-592, 2017
• R. C. Gonzalez, "Deep Convolutional Neural Networks", in IEEE Signal Processing Magazine, vol. 35, no. 6, pp. 79-87, Nov. 2018
• S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, pp. 1-6, 2017
• S. Lu, Z. Lu, S. Aok and L. Graham, "Fruit Classification Based on Six Layer Convolutional Neural Network," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, pp. 1-5,2018.
• Kseniya P. Korshunova,” A Convolutional Fuzzy Neural Network for Image Classification”, IEEE, 2018