Computer Vision Radar for Autonomous Driving using Histogram Method

Abstract

Mobility is a fundamental human desire. All societies aspire to safe and efficient mobility at low ecological and economic costs. ADAS systems (Advanced Driver Assistance Systems) are safety systems designed to eliminate human error in driving vehicles of all types. ADAS systems such as Radars use advanced technologies to assist the driver while driving and thus improve their performance. Radar uses a combination of sensor technologies to perceive the world around the vehicle and then provide information to the driver or take safety action when necessary. Conventional radars based on the emission of electromagnetic and ultrasonic waves have been consumed in the face of the challenges of the constraints of modern autonomous driving, and have not been generalized on all roads. For this reason, we studied the design and construction of a computer vision radar to reproduce human behavior, with a road line lane detection approach based on the histogram of the grayscale image that gives good estimates in real-time, and make a comparison of this method with other computer vision methods performed in the literature: Hough, RANSAC, and Radon.