Browse > Article

A Study on the extraction of activity obstacles to improve self-driving efficiency  

Park, Chang min (영산대학교 성심교양대학)
Publication Information
Journal of Platform Technology / v.9, no.4, 2021 , pp. 71-78 More about this Journal
Abstract
Self-driving vehicles are increasing as new alternatives to solving problems such as human safety, environment and aging. And such technology development has a great ripple effect on other industries. However, various problems are occurring. The number of casualties caused by self-driving is increasing. Although the collision of fixed obstacles is somewhat decreasing, on the contrary, the technology by active obstacles is still insignificant. Therefore, in this study, in order to solve the core problem of self-driving vehicles, we propose a method of extracting active obstacles on the road. First, a center scene is extracted from a continuous image. In addition, it was proposed to extract activity obstacles using activity size and activity repeatability information from objects included in the center scene. The center scene is calculated using region segmentation and merging. Based on these results, the size of the frequency for each pixel in the region was calculated and the size of the activity of the obstacle was calculated using information that frequently appears in activity. Compared to the results extracted directly by humans, the extraction accuracy was somewhat lower, but satisfactory results were obtained. Therefore, it is believed that the proposed method will contribute to solving the problems of self-driving and reducing human accidents.
Keywords
Activity obstacles; Center scene; Activity size; Activity repeatability; Self- driving;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hong LU, Yap-Peng Tan, "An efficient graph theoretic approach to video scene clustering," Information, Communication and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Processing of the 2003 Joint. Conference of the Fourth International Conference on, Vol. 3, pp.1782-1786, 15-18 Dec. 2003
2 W. Y. Ma and B. S. Manjunath, "NETRA: A Toolbox for Navigating Large Image Databases," Proc. IEEE International Conference on Image Processing, Santa Barbara, Vol. 1, 1997, pp. 568-571.
3 E. Park, C. Yu, and J. Choi, "Development of a lateral control system for autonomous vehicles using information fusion of vision and IMU sense", Journal of Institute of Control Robotics and Systems, Vol. 21, No. 3, pp.179-186, Mar. 2015.   DOI
4 U. Lee, S. Yun, I. Shim, S. Shin, J. Choi, J. oh, H. Shim, I. Kwon, and S. Choi, "Development of autonomous vehicles capable of environmental awareness and collision avoidance on complex roads", Journal of Institute of Robots and humans, Vol. 10, No. 2, pp.20-31, May 2013.
5 G. An et al "Vehicle/driving autonomous driving technology", Journal of electronic engineering, Vol. 41, No. 1, pp. 30-37, 2014
6 C. Badue, R. Guidolini, R. Carneiro, P. Azevedo, V. Cardoso, A. Forechi, L. Jesus, R. Berriel, T. Paixao, F. Mutz, L. Veronese, T. Santos, and A. Souza, "Self-Driving Cars: A Survey", arXiv: 1901.04407v2, 2019
7 H. Kim, W. Kang, B. Park, C. Roh, Y. Kim, and L. Lim, "Improved Road Infrastructures to Strengthen Driving Safety of Automated Driving Car", KICT 2019-055, Dec. 2019
8 S. Chang, W. Chen, H. Meng, H. Sundaram, and D. Zhong, "VideoQ: An automated content based video search system using visual cues," Proc of ACM Multimedia Conference, Nov. 1997