Browse > Article
http://dx.doi.org/10.6109/jkiice.2020.24.7.834

Steering Control of an Autonomous Vehicle Using CNN  

Hwang, Kwang-Bok (Dept. of Mechatronics Eng., Gyeognam Nat. Univ. of Science and Technology)
Park, Jin-Hyun (Dept. of Mechatronics Engineering, Gyeognam Nat. Univ. of Science and Technology)
Abstract
Among the autonomous driving systems based on visual sensors, the control method using a vanishing point is the most general method for autonomous driving. However, if the lane is lost or does not exist, it is very difficult to detect this and estimate the vanishing point. In this paper, we predict the vanishing point of the road and the vanishing point lines on the left and right sides using CNN for the camera image and design the steering controller for autonomous driving from the predicted results. As a result of the simulation, it was confirmed that the proposed method well tracked the center of the road regardless of the presence or absence of a solid lane, and was superior to the control method using a general method using the vanishing point.
Keywords
CNN(Convolutional Neural Network); lane detection; steering controller; vanishing point;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. P. Kingma, and J. Ba, "Adam: A method for stochastic optimization," Available: arXiv preprint arXiv:1412.6980, 2014.
2 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.
3 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.   DOI
4 K. H. An, S. W. Ah, W. Y. Han, J. C. Son, "Autonomous car technology," Telecommunication Trend Analysis Report, 2013.
5 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.
6 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.   DOI
7 Q. Han, K. Zhao, J. Xu, and M. M. Cheng, "Deep Hough Transform for Semantic Line Detection," Available: arXiv preprint arXiv:2003.04676, 2020.
8 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.
9 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.   DOI
10 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.
11 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.   DOI
12 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.
13 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.
14 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.
15 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.
16 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.
17 Unity Technologies, Build the skills to bring your vision to life [Internet], Available: http://unity.com.
18 A. Gulli, and P. Sujit, Deep learning with Keras, Packt Publishing Ltd, 2017.