International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958 (Online), Volume-9 Issue-4, April 2020 1989 Published By: Blue Eyes Intelligence Engineering & Sciences Publication © Copyright: All rights reserved. Retrieval Number: D9050049420/2020©BEIESP DOI: 10.35940/ijeat.D9050.049420 Journal Website: www.ijeat.org Abstract: The introduction of modern and more advanced vehicles has stretched their performance boundaries dramatically in terms of pace and maneuverability. It has also significantly enhanced the likelihood of people losing control of their vehicle, contributing to accidents. Within the past, several strategies have been suggested which resolve this issue by restricting the car's travel to just one specific path. To various road situations, this is achieved by applying lane identification utilizing algorithms such as canny edge identification, Hough transformations, vanishing point estimates, principal component analysis etc. Practical deployment of these programs, however, requires extremely powerful hardware such as TDA3x which can process in real time. This paper aims to introduce the usage of the Lane Detection and Alert System on a Texas Instruments Driver Assist 3x (TDA3x) board with a frame resolution of (1920 * 1080p) at 2 GHz relative to the current implementation, which has a resolution of only (480 * 270p) at 100 MHz [16]. This system too makes use of canny edge detection and hough transforms to identify the lane points, and tracks the vehicle movement by extracting the corresponding polar co-ordinates. Keywords : Lane Detection and Warning System (LDWS), Canny edge identification, Hough transforms, Vanishing point estimates, Principal component analysis, TDA3x I. INTRODUCTION There could be different causes for an on-road collision. Nonetheless, the split-second pause on the driver's part in reacting to an incident is what determines the condition between life and death. In most instances, accidents result when the driver has little time to react to an impending accident. This is attributed to the loss of synchronization between a driver's body and mind. There are also situations under which the crash is triggered by pure incompetence on the part of the vehicle driver. Whatever could be the cause, a careful rider's timely reaction is only one remedy that may potentially avert a crash and in effect save the lives of others. Because humans are likely to commit mistakes intentionally or unknowingly, they cannot be expected to assume complete responsibility for maintaining fellow travelers' safety. Within the car an automated device will be built and introduced that can operate independently of the Revised Manuscript Received on April 25, 2020. * Correspondence Author Suhas N Bhargav*, department of Electronics and Communication Engineering, R V College of Engineering, Bengaluru, India. Email: suhas96bhargav@gmail.com Mrs. Rajani Katiyar, department of Electronics and Communication Engineering, R V College of Engineering, Bengaluru, India. Email: rajanikatiyar@rvce.edu.in © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) human driving the automobile. The purpose of this program is to control the driver's behavior constantly whilst maintaining track of the ambient environments at the same time. The benefit of putting up these devices inside the vehicle is that they can detect the driver's mistakes and anticipate future crashes as well. Not only does this warn the driver of his errors, but it would also help him resolve such errors by taking corrective steps on time. In this article, Lane Detection and Warning System (LDWS) usecase would be addressed among the other uses that the autonomous systems will have. As the name itself indicates, this program essentially collects video frames from the camera on the front of the vehicle, and then extracts details about the lane lines on the route. The progress of the car will be monitored on the basis of the available details and an appropriate alert will be given to the driver beforehand. Not only does this warn the driver, but it will also help to prevent potential crash until it is too late for the driver to react. Lane marks were identified on the path by evaluating the double edges of the lane markings [1]. On the captured image, canny edge detection operation was conducted to collect the details about the edges of the road. Then the essential features of the lane markers were derived by making use of this lane edge information and the lane field details. In the other side, [2] sought to concentrate the research in increasing the processor speed rather than on the precision of the usecase. Throughout this method, it was discovered that the usecase processing speed could be significantly improved by the resolution of the captured image, without any loss in precision or efficiency. [3] and [4] concentrated on the noise dimension of the picture by seeking to increase the efficiency and adaptability of the illumination to adjust to various lighting conditions on the route. However, there were drawbacks to their function in the presence of motorcyclists and pedestrians. [5] attempted to demonstrate the particular bottlenecks found in current procedures, such as obstructions created by the movement of cars, ambiguous lane markers, shadow obstructions attributable to houses, etc. and attempted to include several workarounds. [6] and [7] also based their research on solving the question of low road visibility in hilly regions due to the prevalent complex weather conditions including heat, fog, haze, etc. While they were able to obtain an output of 82% and 92% respectively, their job had some drawbacks near to the tight curves or in the midst of pedestrians. In [8], a binarization method was applied to the research regions, where each sample area was further divided into two parts: the foreground region and the context zone. This was achieved to make sure the context data did not impact the algorithm's overall output. Advanced Driver Security Application Suhas N Bhargav, Rajani Katiyar