Self Driving cars have continued to be in the limelight for a couple of years and even now when technology giants like Uber, Lyft, Apple and manufacturing giants like Volvo, Toyota invest heavily they must be worth a huge treasure. Self driving cars can solve a lot of problems we face on roads. Delay due to traffic, vehicle collision which ultimately leads to road rage as we experience and the ever growing noise pollution. Self driving cars could provide us with the ultimate freedom to drive. Imagine not having to worry about which route to take to reach our destination faster, and above all with the supreme experience to sit back and be free or continue with our work. Time efficiency if we take into account could free us from the necessary task of having to drive and could reduce the time we waste in commuting. Apart from all the good the Self Driving vehicles boast of there is still a question about pedestrian safety. How and what better technologies can be equipped to ensure pedestrian safety remains the need of the hour. This project aims to be solving this problem by adopting use of several transformation techniques for providing a finite lane path for smooth commute of the self driving cars.
Traffic accidents have become one of the most serious problems in today's world. Roads are the mostly chosen modes of transportation and provide the finest connections among all modes. Most frequently occurring traffic problem is the negligence of the drivers and it has become more and more serious with the increase of vehicles. Increasing the safety and saving lives of human beings is one of the basic function of Intelligent Transportation System (ITS). There are still questions about the safety of pedestrians traveling or crossing the road or how would a Self Driving Car perform a smooth commute from 2 different points. These road accidents can be reduced with the help of road lanes or white markers that assist the driver to identify the road area and non-road area and also detecting the pedestrians crossing the roads. In this project we are going to solve this problem of pedestrian and road lane detection for self-driving cars using various image processing techniques. We will be using HOG (Histogram of Oriented Gradients) for the detection and Kalman Filter for tracking and prediction of pedestrians. For lane detection, Bilateral filter will be used for reducing noise followed by edge detection and mapping of these edge points in finite lines for lane detection using thresholding and Hough transform .
