Lane Detection for Autonomous Vehicle Management: PHT Approach

Abstract

Road region extraction is a crucial part of the vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces road accidents, enhances safety, and improves traffic conditions. Autonomous Guided Vehicles are capable of performing required tasks in a defined environment without continuous human guidance. This research paper presents the design of a prototype autonomous guided vehicle which will detect and follow the lanes using the Probabilistic Hough Transform (PHT) algorithm. To do so, We convert our RGB road images into an HSV color model and then apply Gaussian smoothing to the converted grayscale image. For detection purposes, we process our region of interest (ROI) using a polygon clipping algorithm. Then, we apply Probabilistic Hough Transform upon the ROI image while setting all the parameters in our proposed lane detection algorithm. We present a robust real-time approach to extract road regions even in critical conditions like urban roads, unmarked roads. We have applied our proposed framework on the CALTECH dataset and gained 94.7% detection accuracy results in our experimental setup.