Convolutional Neural Network Computation for Steering Angle Prediction Based on Road Direction
DOI:
https://doi.org/10.32628/IJSRSET207323Keywords:
Self-driving car, CNN , Steering angle, CNN for steering angle.Abstract
Self driving vehicle is a vehicle that can drive by itself it means without human interaction . This system shows how the computer can learn and the over the art of driving using machine learning techniques. Therefore for a car achieving the autonomous ability it must show the control of human activities while driving. Those activities include control of steering wheel. There exist different techniques to control the steering angle and one of them is CNN. In this article we are going to see how CNN can be used to predict the steering angle.
References
- Zhilu Chen and Xinming Huang, :"End-To-end learning for lane keeping of self-driving cars", IEEE Intelligent Vehicles Symposium Proceedings, 2017.
- Rodolfo Valiente ; Mahdi Zaman ; Sedat Ozer ; Yaser P. Fallah: Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
- Chen, M., Jochem, T., & Pomerleau, D. “AURORA: A vision-based roadway departure warning system,” Intelligent Robots and Systems 95.'Human Robot Interaction and Cooperative Robots', Proceedings. 1995 IEEE/RSJ International Conference on Vol. 1, pp. 243-248.
- Deng, J., & Han, Y. “A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting,” Proceedings of the 2013 Research in Adaptive and Convergent Systems , pp. 157-164.
- Wang, Y., Teoh, E. K., & Shen, D. “Lane detection and tracking using B-Snake,” Image and Vision computing, 22(4), 2004, pp. 269-280.
- Aly, M. “Real time detection of lane markers in urban streets,” Intelligent Vehicles Symposium, 2008 IEEE pp. 7-12.
- Liu, W., Zhang, H., Duan, B., Yuan, H., & Zhao, H. “Vision-based realtime lane marking detection and tracking,” Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on pp49-54.
- Revilloud, M., Gruyer, D., & Pollard, E. “An improved approach for robust road marking detection and tracking applied to multi-lane estimation,”Intelligent Vehicles Symposium (IV), 2013 IEEE, pp. 783- 790.
- Jang, H. J., Baek, S. H., & Park, S. Y. “Lane Marking Detection in Various Lighting Conditions using Robust Feature Extraction,” International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, 2014.
- M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
- M. Bojarski, P. Yeres, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, and U. Muller. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911, 2017.
- A. El Sallab, M. Abdou, E. Perot, and S. Yogamani. Deep reinforcement learning framework for autonomous driving.
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Autonomous Vehicles and Machines, Electronic Imaging, 2017. 14C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Learning hierarchical features for scene labeling. IEEE transactions on pattern analysis and machine intelligence, 35(8):1915–1929, 2013.
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2019-04-30
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[1]
Aires Da Conceicao, Dr. Sheshang Degadwala "Convolutional Neural Network Computation for Steering Angle Prediction Based on Road Direction" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099,
Volume 7, Issue 3, pp.290-295, May-June-2020. Available at doi : https://doi.org/10.32628/IJSRSET207323