Open AccessRoad Lane Detection System for Self Driving Car
Author(s) -
Rakesh Kadu Rakesh Kadu,
Purushottam J. Assudani,
Manvi Jaiswal,
Druvsingh Bist,
Ankush Tickoo
Publication year - 2021
Publication title -
international journal of next-generation computing
Language(s) - English
Resource type - Journals
eISSN - 2229-4678
pISSN - 0976-5034
DOI - 10.47164/ijngc.v12i5.466
Subject(s) - computer vision , artificial intelligence , canny edge detector , hough transform , computer science , road surface , process (computing) , image processing , dashboard , grayscale , enhanced data rates for gsm evolution , automation , edge detection , identification (biology) , video camera , automotive industry , engineering , pixel , image (mathematics) , mechanical engineering , civil engineering , data science , botany , aerospace engineering , biology , operating system
Self-driven car or driverless car is an innovation in the Automobile industry. In a self-driven car, direction requires automation. The detection of road lanes or boundaries is a complex and challenging process. In this process identification of the road and the vehicle position on the road plays an important role. It includes the identification of the road and finding the position of the vehicle and road. This paper presents an image-based road lane detection system for self-driven cars using a front camera. The camera mounted on the front dashboard will capture a video stream and apply the processing to detect the road lane. The images will be extracted from continuously captured video and will apply various image processing algorithms like greyscale, Gaussian blur, Canny edge detection, Hough transform to detect and construct the lane which is used to guide the car on road. The system will generate the video based on a processed image which is used to further control the car movement.