Advanced Lane Finding Project

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.

Camera Calibration

The code for this step is contained in the first code cell of the IPython notebook called lanedetection.ipynb.

I start by preparing "object points", which will be the (x, y, z) coordinates of the chessboard corners in the world. Here I am assuming the chessboard is fixed on the (x, y) plane at z=0, such that the object points are the same for each calibration image. Thus, objp is just a replicated array of coordinates, and objpoints will be appended with a copy of it every time I successfully detect all chessboard corners in a test image. imgpoints will be appended with the (x, y) pixel position of each of the corners in the image plane with each successful chessboard detection.

I then used the output objpoints and imgpoints to compute the camera calibration and distortion coefficients using the cv2.calibrateCamera() function. I applied this distortion correction to the test image using the cv2.undistort() function and obtained this result:

Pipeline (single images)

1. Distortion-corrected image.

To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one:

2. Color transforms, gradients or other methods to create a thresholded binary image.

I used a combination of color ,gradient and directional thresholds to generate a binary image (thresholding steps at lane detection pipeline in lanedetection.ipynb file). Here's an example of my output for this step.

3. Perspective transform and provide an example of a transformed image.

The code for my perspective transform appears under the heading Perstective transform to bird's eye view in the file lanedetection.ipynb file (output_images/examples/example.py) (or, for example, in the 14th code cell of the IPython notebook). The cell takes as inputs an image (img), as well as source (src) and destination (dst) points. I chose to hardcode the source and destination points in the following manner:

src = np.float32(
    [[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
    [((img_size[0] / 6) - 10), img_size[1]],
    [(img_size[0] * 5 / 6) + 60, img_size[1]],
    [(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
dst = np.float32(
    [[(img_size[0] / 4), 0],
    [(img_size[0] / 4), img_size[1]],
    [(img_size[0] * 3 / 4), img_size[1]],
    [(img_size[0] * 3 / 4), 0]])

This resulted in the following source and destination points:

Source	Destination
585, 460	320, 0
203, 720	320, 720
1127, 720	960, 720
695, 460	960, 0

I verified that my perspective transform was working as expected by drawing the src and dst points onto a test image and its warped counterpart to verify that the lines appear parallel in the warped image.

4. Lane-line pixels and fit their positions with a polynomial?

I used the sliding window approach to detect the lane pixels in the current image frame then fit my lane lines with a 2nd order polynomial kinda like this:

5. The radius of curvature of the lane and the position of the vehicle with respect to center.

I did this in the cell 13 under Image pipeline in my code in lanedetection.ipynb file. I used the radius of curvature equation and scaled it to real world coordinate by multiplying it with pixel to meter ratio.

6. Results

Dependencies

[1] OpenCV
[2] Numpy
[3] Movie.py

Run Instructions

jupyter notebook lanedetection.ipynb 

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Discussion

Pipleline will likey fail when we come across image frames where it is difficult to figure out the lane edges using image thresholding and gradient techniques which will result in failure of the activated pixels detection. The possible improvements for these scenarios would be to tune the image thresholding pipline parameters over a larger variety of test images and to perform the lane polynommial smoothening over the last n frames.