References
- K. V. Wees, and K.l Brookhuis, "Product liability for ADAS; legal and human factors perspectives," European Journal of Transport and Infrastructure Research, vol. 5, no. 4, pp. 357-372, 2020.
- F. Jimenez, J. E. Naranjo, J. J. Anaya, F. Garcia, A. Ponz, and J. M. Armingol, "Advanced driver assistance system for road environments to improve safety and efficiency," Transportation Research Procedia, vol. 14, pp. 2245-2254, 2016. https://doi.org/10.1016/j.trpro.2016.05.240
- K. H. An, S. W. Ah, W. Y. Han, J. C. Son, "Autonomous car technology," Telecommunication Trend Analysis Report, 2013.
- P. L. Serra, P. H. Masotti, M. S. Rocha, D. A. de Andrade, W. M. Torres, and , R. N. de Mesquita, "Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN)," Progress in Nuclear Energy vol. 118:103133, pp. 1-21, 2020.
- J. Matas, G. Charles, and J. Kittler, "Robust detection of lines using the progressive probabilistic hough transform," Computer Vision and Image Understanding, vol. 78, no. 1, pp. 119-137, 2000. https://doi.org/10.1006/cviu.1999.0831
- Q. Han, K. Zhao, J. Xu, and M. M. Cheng, "Deep Hough Transform for Semantic Line Detection," Available: arXiv preprint arXiv:2003.04676, 2020.
- A. Matessi, and L. Lombardi, "Vanishing point detection in the hough transform space," in European Conference on Parallel Processing, Springer, Berlin, Heidelberg, pp. 987- 994, 1999.
- J. Xiao, W. Xiong, Y. Yao, L. Li, and R. Klette, "Lane Detection Algorithm Based on Road Structure and Extended Kalman Filter," International Journal of Digital Crime and Forensics, vol. 12, no. 2, pp1-20, 2020. https://doi.org/10.4018/IJDCF.2020040101
- N. S. Aminuddin, "A new approach to highway lane detection by using Hough transform technique," Journal of Information and Communication Technology, vol. 16, no. 2, pp. 244-260, 2020.
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradientbased learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791
- W. Shang, K. Sohn, D. Almeida, and H. Lee, "Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear units," in Proceedings of the 33rd International Conference on Machine Learning, New York: NY, pp. 2217-2225, 2016.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
- G. E. Dahl, T. N. Sainath, and G. E. Hinton, "Improving deep neural networks for LVCSR using rectified linear units and dropout," in Conference of Acoustics, Speech and Signal Processing, Vancouver: BC, pp. 8609-8613, 2013.
- Y. Bengio, "Deep Learning of Representations for Unsupervised and Transfer Learning," in Proceedings of ICML Workshop on Unsupervised and Transfer Learning, Washington:WA, pp. 17-37, Jul. 2012.
- S. C. Park, K. B. Hwang, H. M. Park, Y. K. Choi, and J. H. Park, "Application of CNN for steering control of autonomous vehicle," in Conference of the Korea Institute of Information and Communication Engineering, vol. 22 no. 1, pp. 234-235, May. 2018.
- Unity Technologies, Build the skills to bring your vision to life [Internet], Available: http://unity.com.
- A. Gulli, and P. Sujit, Deep learning with Keras, Packt Publishing Ltd, 2017.
- D. P. Kingma, and J. Ba, "Adam: A method for stochastic optimization," Available: arXiv preprint arXiv:1412.6980, 2014.