My submission of Udacity Self Driving Car Term1, Project 4. This project follows further from Project 1. It employs advanced computer vision techniques to generalize a lane detection pipeline. Previous effort only detects lanes that are straight lines. In the project, we are able to detect curved lines.
Sample video output:
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
You might need to follow instructions on CarND Term1 Starter Kit before running the notebook.