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 I 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.

For this project, a great writeup should provide a detailed response to the "Reflection" section of the project rubric. There are three parts to the reflection:

1. Describe the pipeline

2. Identify any shortcomings

3. Suggest possible improvements

The writeup can be found here.

Step 1: Set up the CarND Term1 Starter Kit if you haven't already.

Step 2: Open the Jupyter Notebook

Fork this repository and clone it to your local machine. Open up the IPython Notebook and run all the cells. The process_image() method is used to identify and draw the lane lines in an image. Cell numbers 8, 10, and 12 all show examples of using the process_image() method to prcess video clips. You can use these examples to run the API for your own video files.

Step 3: The results of my lane finding algorithm are found here.