Built an advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding. Identified lane curvature and vehicle displacement. Overcame environmental challenges such as shadows and pavement changes

In this project, I have used computer vision techniques to identify lane boundaries and compute the estimate the radius of curvature given a frame of video of the road.

To achieve this, the following steps are taken:

• Computed the camera calibration matrix and distortion coefficients of the camera lens used given a set of chessboard images taken by the same camera
• Used the aforementioned matrix and coefficient to correct the distortions given by the raw output from the camera
• Use color transforms, and sobel algorithm to create a thresholded binary image that has been filtered out of unnecessary information on the image
• Apply perspective transform to see a “birds-eye view” of the image as if looking from the sky
• Apply masking to get the region of interest, detect lane pixels,
• Determine the best fit curve for each lane the curvature of the lanes
• Project the lane boundaries back onto the undistorted image of the original view
• Output a visual display of the lane boundaries and other related information

• You need to setup dependencies to run a Jupyter Notebook on your computer and setup a handful of packages such as opencv

import matplotlib.pyplot as plt
%matplotlib inline
import cv2 
import numpy as np
import pickle
from moviepy.editor import VideoFileClip

• This is so that each notebook sees to see relevant files, the most relevant files being the python classes.

• Medium article

• Submitted detailed technical writeup

• Udacity's project page

• ChessBoard - chessboard.py
• BirdsEye - birdseye.py
• LaneFilter - lanefilter.py
• Curves - curves.py