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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.
• Apply a perspective transform("birds-eye view").
• Use color transforms, gradients, etc., to create a thresholded binary image.
• 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.

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Camera Calibration

Using cv2.findChessboardCorners, cv2.calibrateCamera, cv2.undistort I calibrated the camera to obtain undistorted images.

The next image shows an example of undistorted image and the area of interest.

Pipeline (single images)

1. Perspective transform

After the calibration step, I applied a perspective transform using cv2.getPerspectiveTransform and cv2.warpPerspective with source and destination points:

 src = np.array([ [550, 450], [750,450], [1200, 700], [100, 700] ], dtype='float32')
dst = np.array([ [ofs, ofs], [m, ofs], [m, n], [ofs, n] ], dtype='float32')

The perspective transform return an image where we can easily measure distances.

2. Gradient and Color thresholding

I used a combination of color and gradient thresholds to generate a binary image. In particular I decided to combine cv2.Sobel in the x direction and cv2.cvtColor to extract the S-Channel in the HLS color space and the L-Channel in the LAB color space.

3. Lines detection

Then, using histogram, windows polynomial fit I obtained the two lines of interest.

4. Radius of curvature and position.

In the end I computed radius (in meters) and position of the vehicle respect to the center.

5. Result

Here an example of what I obtained drawing this line on the original image.

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Pipeline (video)

Finally I defined a class Line where I compute line, radius of curvature and position respect center for every frame; I decided to average over the last 10 frames to obtain a smoother result; I also check for undetected lines in particular frames.

Here's a link to my video result

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Discussion

problems and improvement

My implementation is not sophisticated: there is a simple outliers' detection and a simple check for maximal difference between lines in sequential frames.