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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.

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SABKI/Project-Finding-Lane-Lines-on-the-Road

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Finding Lane Lines on the Road

Udacity - Self-Driving Car NanoDegree

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Overview

When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are act as our constant reference for where to steer the vehicle. Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm.

In this project you will detect lane lines in images using Python and OpenCV. OpenCV means "Open-Source Computer Vision", which is a package that has many useful tools for analyzing images.

To complete the project, two files will be submitted: a file containing project code and a file containing a brief write up explaining your solution. We have included template files to be used both for the code and the writeup.The code file is called P1.ipynb and the writeup template is writeup_template.md

To meet specifications in the project, take a look at the requirements in the project rubric

Please refer to the writeup pdf (write up 1st project_Finding Lane Lines on the Road.pdf) for much details about this project.

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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.

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