A Vision-Based Unstructured Road Detection Algorithm for Self-driving Cars

TLDR: In this paper, a novel vision-based road detection technique is proposed, which uses noise to enhance the road edges in the image and unstructured straight road is detected using Hough Transform.

Abstract: Unstructured road detection is one of the difficult tasks for self-driving cars than the detection of road with proper lane markings. Also, it is an extremely difficult task to detect the highly deteriorated district and taluk roads using currently available vision-based algorithm; as the exposed gravels and grass covering on both sides (edges) of road adds more noise in the input image. To address this issue, a novel vision-based road detection technique is proposed in this research work. This new method uses noise to enhance the road edges in the image and unstructured straight road is detected using Hough Transform. This paper is divided into three parts: bird’s eye view transformation of 2D road image received from the vehicle camera to correct the perspective distortion and easier identification of Region of Interest (ROI), addition of noise in the ROI of image to differentiate the valid road from the background and use of Hough Transform to identify the edges of unstructured road having no road markings. Finally, we present a simple way to find the centerline on the detected road for departure warning to reduce the additional computation. The simulation results corroborate that the proposed method detects the road successfully and can be used in real-time detection system.