OBJECT DETECTION AND LANE CHANGING FOR SELF DRIVING CAR USING CNN

In recent years, many companies are working on the development of autonomous cars. Every citizen on the planet is concerned about their safety. In the framework of Advanced Driver Assistance Systems, one of the main goals is to improve safety and reduce road accidents, ultimately saving lives. One of the most difficult jobs in an autonomous driving system is detecting road lanes or road boundaries. Lane and object detection may be a crucial component of collision prevention in driving assistance systems. With the growth in traffic, there is a demand for more security and comfort when driving, it impose the development of new technology. Computer vision is one of the ways that may be utilized to assist the driver in difficult scenarios in order to improve his safety and comfort. The initial layer of autonomous vehicles' capabilities is lane tracking. Many sensors, including as lasers, radar, and vision sensors, are commonly employed for obstacle and lane detection. Computer vision is one of the primary method for detecting road limits and lanes using a vehicle's vision system. The technology uses a camera installed on the vehicle to capture the front view, then uses a few algorithms to detect lanes and objects. The lanes and objects are detected using a flexible algorithm. This paper focuses on a computer vision-based object detection technique by using CNN algorithm.