Simultaneous improvement of obstacle detection accuracy and speed in cut stereo vision with ultrasonic data in smart vehicles

Document Type : Research Paper

Authors

1 PhD. Student of Mechanical Engineering, Iran University of Science and Technology

2 Dept of Mechanical Engineering, Director, Intelligent, Distributed and autonomous Systems (IDAS) Laboratory, Iran University of Science and Technology, Tehran, IRAN,

3 Dept. of Mechanical Engineering, Iran University of Science andTechnology, Tehran, IRAN

Abstract

Nowadays, traffic has become a challenge for everyone. One of the ways to overcome this problem is to make cars more intelligent. In this research, perception enhancement of the environment has been considered by using the data fusion of ultrasonic data and stereo vision. In this research, the researchers have suggested the cut stereo vision method by ultrasonic data in such a way that the accuracy and speed of obstacle detection will increase simultaneously in smart cars. Therefore, in addition to the similarity of the light intensity in the matching window, the neighboring pixels' depth has been used in a way to achieve the goals mentioned above without exponentially increasing the computational load. So, two types of compliance windows have been defined. One window is similar to the cut stereo method, and the other is called the inference window. By using the proposed inference window based on cut stereo vision, it is possible to apply the influence of the depth of neighboring pixels at a sufficient and effective level, which is reduced 61.57% error compared to the previous study. Furthermore, according to the suggested way of implementation on the graphic card, the obstacle detection speed has improved by 43.93% compared to the previous parallel implementation. The mentioned improvements make it possible to detect the environment in each meter of a car's movement if its speed is 178.1 km/h rather than 100.8km/h in the previous study. So current study produces smart cars more reliable.

Keywords

Journal of Machine Vision and Image Processing
Volume 10, Issue 3
September 2023
Pages 65-77

Files

Share

How to cite

Statistics