Advanced Lane Finding Project

Or how I detected the curved lanes for self driving cars.

For complete implementation of the project:

The goals of the project are the following:

  • Compute the camera matrix and distortion coefficients given a set of chessboard images.
  • Apply distortion correction to raw images.
  • Use color and gradients transforms to create a binary image.
  • Apply a perspective transform to birds-eye view of the binary image.
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the radius of curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Visualize the lane boundaries and numerical estimation of lane curvature and vehicle position.

Camera Calibration

Starting point is the preparation of “object points”, which will be the (x, y, z) coordinates of the chessboard corners in the world.

Assumption: 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.

Source Code Reference File implementation/ Method PreProcessing.get_calibration_params()

The algorithm is as follows:

  • Read the source image.
  • Find the corners of the image using opencv findChessboardCorners() and append the corners in the image points.
  • Find the camera matrix and distortion coefficients using opencv calibrateCamera().
  • Save the calibration parameters as a pickle file for reuse later.

The results of the camera calibration and distortion removal:

Right side: Original Image. Left side: Undistorted Image

Distortion Correction applied on Chessboard


1. Distortion Correction:

The Algorithm for thresholding is as follows:

  • Load the calibration parameters i.e Camera Matrix and Distortion Coefficient from a pickle file.
  • Apply calibration parameters on the source image to remove distortion using opencv undistort().

To demonstrate this step, I will apply the distortion correction to the real world conditions:

Right side: Original Image. Left side: Calibrated Image

Distortion correction applied on real world conditions.

2. Color and Gradient Thresholding:

The Algorithm for thresholding is as follows:

  • Apply grayscale Sobel X using opencv Sobel method.
  • Find the 8bit Sobel and binary Sobel using np.uint8(255 * sx_abs / np.max(sx_abs)).
  • Get binary R channel from RGB using r_binary[(r>=rgb_thresh[0])&(r<=rgb_thresh[1])]=1.
  • Get binary S channel from HLS.
  • Resultant is the merger of binary Sobel and binary S channel AND'd with binary R channel.

Threshold For Low High Smoothing Kernel Sobel X 20 200 9 R channel 170 255 — S channel 120 255 -

Right side: Original Image. Left side: Binary Image

Binary images using SobelX, R channel and S channel

3. Perspective Transform:

  • The implementation method to get the perspective transform src and dst points is get_perspective_points(). This method takes as input input_image and optional offset values.
  • The implementation method to get the warped image using src and dst points is get_wrapped_image(). The method takes as input input_image, source and destination points and returns warped image.
  • The values I chose for src and dst points is such that it covers the Lane Trapezoid in both original and warped images.

This resulted in the following source and destination points:

Source Destination 100, 720 100, 1280 585, 450 100, 0 695, 450 620, 0 1180, 720 620, 1280

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.

Right side: Original Image. Left side: Warped Image

Perspective transform to get the Bird Eye View

4. Lane Lines Detection using Histogram and Sliding Window Algorithm:

The Algorithm for detecting lane lines is as follows:

  • Take histogram of the bottom half of the image.
  • Find peaks in left and right of the image. These peaks represent the lanes.
  • Identify x and y positions of all nonzero pixel points.
  • Loop over windows and for each window:
  • Identify window boundary.
  • Find nonzero pixel in x and y within window boundary and append them in good_indices list.
  • Extract the left and right xy position from nonzero pixel using good_indices.
  • Apply 2nd order polynomial to the left and right pixel positions. This gives us the left and right lines polynomial fit.

The Algorithm for updating the lane lines detected is as follows:

  • Since we have already found lane lines in the previous step, we don’t need to perform blind search each time, instead we can use the information of previously found lines fits and search in the region around them.
  • Get left and right indices for nonzero pixels.
  • Get left and right pixel positions from nonzero pixels.
  • Apply 2nd order polynomial to the left and right pixel positions.
Original warped Images
Detected lanes using Histogram and Sliding Window algorithm

5. Radius of curvature and vehicle distance from center lane:

Algorithm for finding radius of curvature is as follows:

  • Define pixel to meter conversion factor.
  • Apply conversion factor on left and right polynomial fits. This gives us polynomials in meter.
  • Find radius of curvature R = ((1+ (f')**2)**1.5)/f'' where f' means 1st derivative and f'' means 2nd derivative.

Algorithm for finding vehicle distance from center lane is as follows:

  • Get car position which is center of the image.
  • Get lanes width by taking difference of left and right polynomial fits.
  • Get lane center using midpoint left and right polynomial fits.
  • Get distance from center by taking difference of car position and lane center.
  • Get distance in meters by multplying distance from center with conversion factor.

6. Results:

Here are the examples:

Complete pipeline with estimated curvature and vehicle distance from center of the lane.

Here is the video of the complete pipeline:


Possible Improvements:

  • Using data from left fit only if right fit is deviating significantly and vice verse.
  • Dynanmic thresholding for binarization.
  • Deep learning approach can be used along with current implementation to reduce the dependency on perspective transform and window sliding algorithm.

Potential failure points for current pipeline:

  • Varying light conditions and trees shadow.
  • Pipeline will most definitely fail in snow conditions.
  • Lane lines are obstructed by another vehicle in front.
For complete implementation of the project:
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