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This repository contains a collection of projects focused on the development of self-driving car technology. The work demonstrates practical applications of key concepts in autonomous systems.
The first project in the Udacity Self-Driving Car Nanodegree is about implementing a pipeline that detects lane lines in images. While the pipeline is created for a single image, it can be applied to video footage by breaking the video down into frames, passing the frames through the pipeline, and then reconstructing the video.
Lane Finding Project for Self-Driving Car Nano Degree Term 1. Road Lane Lines are detected by using various image processing techniques like Grayscale, Blurring, Canny Edge Detection, Hough Transform and Masking.
Lane Finding Project for Self-Driving Car Nano Degree Term 1. Road Lane Lines are detected by using various image processing techniques like Grayscale, Blurring, Canny Edge Detection, Hough Transform and Masking.
A lane detection pipeline by tracking white and yellow colored lane lines using self adjusting Canny edge detector implemented in HSL color space to make it intensity invariant.
Detecting Lanes for Self-Driving-Cars... I have developed a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car
Advanced lane line detection using perspective transformation and gradient and color image thresholding - implemented as part of the Udacity Self Driving Car NanoDegree