Finding Lane Lines on the Road

Udacity Self Driving Car Nanodegree - Project #1

2017/6/1

Overview

This project detects lane lines in images by applying color/region masks, Canny edge detection, Hough transform for determining lines, and setting the left/right lanes by a weighted linear polyfit. The raw left/right lines and the final detected left/right lanes are overlaid on the original image as the output.

My project results are shown in the project writeup and the videos linked below.

White lane line video:

Yellow lane line video:

Optional challenge video:

Files

File	Description
P1.ipynb	IPython notebook with all project code
P1_final_code.py	Python code extracted from notebook and reformatted as stand-alone
P1_rawcode.py	All raw Python code extracted from notebook
P1_writeup.md	The project writeup explaining the results
/examples/	Example images and videos included with original project assignment
/test_images/	Road images provided to test algorithm
/test_images_output/	Images generated by algorithm to show each step of pipeline
/test_videos/	Driving videos provided to test algorithm
/test_videos_output/	Driving videos generated by algorithm to show lane detection
/writeup_screenshots/	Images used in project writeup

The original Udacity project repository is here.

How to Run Code

1. Set up Udacity Self-Driving Car Term 1 Starter Kit environment (Python 3)

2. Open the IPython notebook "P1.ipynb" using Jupyter, and execute all cells.