This Article gives and overview of the project.
The goals 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 threshold 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 can find a thorough description here on the detail description.
The environment was created using miniconda, using the following configurations, environment.yml.
The libraries used on this project are:
- numpy
- matplotlib
- opencv
- jupyter
https://github.com/udacity/CarND-Advanced-Lane-Lines
MIT License Copyright (c) 2016-2018 Udacity, Inc.