Lane detection algorithm based on Hough transform for high-speed self driving vehicles. (4th July 2019)

• Record Type: Journal Article
• Authors: Park, Hyunhee
• Is Part Of: International journal of web and grid services. Volume 15:Number 3(2019)
• Page Start: 240
• Page End: 250
• Publication Date: 2019-07-04
• Subjects: lane detection -- Hough transform -- self-driving vehicle -- OpenCV -- TensorFlow; Web services -- Periodicals; Computational grids (Computer systems) -- Periodicals; 006.78
• Languages: English
• ISSNs: 1741-1106

Abstract

This study proposes a lane detection method based on expressway driving videos through a computer vision-based image processing system without using sensors. Both straight and curved sections can occur on a road, and thus, lanes must be detected by quickly determining such sections. The proposed method detects straight and curved sections that are estimated to be lanes using the Hough transform. When lanes are detected from actual images, the scope of left and right lanes is limited to reduce computational load. In this paper, we propose a lane-detection algorithm using the colour space and a stepwise algorithm for accurate lane detection. To verify the proposed algorithms, we developed a small self-driving vehicle model using a TX-2 board. The experiment results when applying the proposed Hough transform algorithm and lane-detection algorithm using the colour space show that the lane detection rate of vehicles driving on curves at high speed is approximately 96%. Through the extensive simulation results, the proposed algorithm to vehicle black boxes or autonomous driving will help prevent lane departure and reduce accident rates.