SIMULATION OF LANE SWITCHING IN SELF DRIVING AUTOMOBILES

The key significance of a self-driving automobile is it is a mechanical contraption that can progress between objectives without human maneuvers, sounds exceptionally essential and clear yet, honestly, this scarcely covers the surface. For a self-driving automobile to come to affirmation, both gear fragments and programming packs are required to compose and construct congruous with each other. In this project, the item points of view vital to producing a model that can make sense of how to drive an automobile in a to a great degree diverse plan of a virtual condition. To content with the software aspects of a self-driving vehicle, we make use of Convolutional Neural Networks (CNN) that works on the idea of regression at its crux. The process involves screen capturing by employing OpenCV while physically driving a vehicle in a gaming console.

DOWNLOADS

> Project Report

> Code

> GitHub

> Jiman Kim, Chanjong Park, “End-to-End Ego Lane Estimation based on Sequential Transfer Learning for Self-Driving Car”, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

> Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller,Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba, “End to End learning for Self-Driving Cars”, Computer Vision and Pattern Recognition

> Lin Li, Yanheng Liu, Heekuck Oh, Jian Wang “Human dynamics based driver model for autonomous car”, IET Intelligent Transport Systems

> Zhilu Chen, Xinming Huang, “End-to-End Learning for Lane Keeping of Self-Driving cars”, 2017 IEEE Intelligent Vehicles Symposium (IV)

> Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao, “Deep Driving- Learning Affordance for Direct Perception in Autonomous Driving”, 2015 IEEE International Conference on Computer Vision (ICCV)

> Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”

> Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee, “Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units”, 33rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48

> Dosovitskiy, A. and Brox, T, “Inverting convolutional networks with convolutional networks”, In CVPR, 2015

> A. Berg, J. Deng, and L. Fei-Fei, “Large scale visual recognition challenge”, 2010, www.imagenet.org/challenges. 2010

> D. Cire ̧san, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification”, Arxiv preprint arXiv:1202.2745 2012

> Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision”, In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pages 253–256. IEEE, 2010

> H. Lee, R. Grosse, R. Ranganath, and A.Y. Ng, “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations”, In Proceedings of the 26th Annual International Conference on Machine Learning, pages 609–616. ACM, 2009

> P.Y. Simard, D. Steinkraus, and J.C. Platt, “Best practices for convolutional neural networks applied to visual document analysis”, In Proceedings of the Seventh International Conference on Document Analysis and Recognition, volume 2, pages 958–962, 2003

> Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning”, IEEE Trans Med Imaging. 2016 Ma