writeup.md

Advanced Lane Finding

Goal

• To identify the road lane boundaries in the video of a camera attached to the car.

OUTPUT

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.

CODE

https://github.com/Mohit-Ak/CarND-Advanced-Lane-Lines/blob/master/advanced_lane_detection.ipynb

Camera Calibration

Code - Section 2 advanced_lane_detection.ipynb

1. Steps and Inference

• Chessboard doesn't have a depth/height and is fixed on the (x, y) plane at z=0 ### Steps
• Calculate object points and image points.
• "object points", which will be the (x, y, z) coordinates of the chessboard corners in a perfect scenario.
• img_points will be appended with the (x, y) pixel position of each of the corners in the image.
• Note - Chessboard size is 9x6
• Method cv2.findChessboardCorners used to find corners
• Method cv2.drawChessboardCorners used to draw the corner
• Method cv2.calibrateCamera used to calibrate camera
• Around three images fail in calibration.

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 Removal

Code - Section 3 advanced_lane_detection.ipynb

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

CHESS BOARD	CAR CAM

2. Image Channels

Code - Section 5 advanced_lane_detection.ipynb

• Tried a number of image channels to see which ones are best suited for lane detection-

3. Gradient and Color threholds

Code - Section 6 advanced_lane_detection.ipynb

• Experimented different gradient and color threshlolds to detect different color lane lines.

• Realized that the following approaches work well.

• Sobel x operator.
• Saturation of HLS channel.
• Hue of HLS channel.

I used a combination of the above mentioned color and gradient thresholds to generate a binary image. Here's an example of my output for this step.

4. Perspective Transform

Code - Section 8 advanced_lane_detection.ipynb

The code for my perspective transform includes a function called perspective_transform(), The perspective_transform() function takes as inputs an image (img), and also the hardcoded source (src) and destination (dst) points to calculate the transform. I chose the 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
170, 720	100, 720
2550, 460	100, 0
745, 460	1100, 0
1200, 720	1100, 720

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. Identified lane-line pixels

Code - Section 9 & 10 advanced_lane_detection.ipynb

Steps

• Take the histogram of the binary thresholded perspetive transformed image. It would give us two spikes for the two lanes present as the total vertical "white" concentration at those regions is higher thatn the rest of the image.
• Use the bottom white points as the starting points and use the sliding window technique to detect the first set of lines.
• Once the first set is detected, we can remember them to concentrate our ROI on the next image which is input instead of doing the histogram again.
• We also remember and average over the past values to remove any camera errors.
• Method sliding_window_polynomial_fit ultimately indentifies the lane with a 2D polynomial(A Parabola).
• polynomialfit_from_previous also indentifies the lane with a 2D polynomial(A Parabola).

Histogram	Sliding Window

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

Code - Section 10 advanced_lane_detection.ipynb

• Method get_curvature
• Define conversions in x and y from pixels space to meters
• 720 px is length of the lane
• 900 px is the average width of the lane
• Note - The image under consideration is perspectively projected.
• Assumption : Camera is mounted at the center of the car and therefore vehicle_offset = deviation of midpoint of the lane is = the deviation from center of image
• Reference : Jeremy shannon's Udacity notes
• The radius of curvature is based upon this website and calculated-

curve_radius = ((1 + (2*fit[0]*y_0*y_meters_per_pixel + fit[1])**2)**1.5) / np.absolute(2*fit[0])

In this example, fit[0] is the first coefficient (the y-squared coefficient) of the second order polynomial fit, and fit[1] is the second (y) coefficient.

• y_0 is the y position within the image upon which the curvature calculation is based (the bottom-most y - the position of the car in the image - was chosen).
• y_meters_per_pixel is the factor used for converting from pixels to meters.

The position of the vehicle with respect to the center of the lane is calculated with the following lines of code:

 car_position = width / 2
 lane_center = (left_fitx[719] + right_fitx[719]) / 2
 vehicle_offset = (lane_center-car_position)*xm_per_pix

6. RESULTS

Code - Section 11 advanced_lane_detection.ipynb

Here are my results on the test images provided by Udacity.

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Pipeline (video)

Here's a link to my video result

OUTPUT

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Discussion

• There was no direct solution to extract the lanes. I had to try different combinations and thresholds.
• Perspective transform values for SRC and DST had to be hadrcoded and experimented. It would be nice to have that part automated too.
• Considered only for 2 lines. There are cases where roads have markings inbetween which are similar to a line. It will be interesting to tackle those scenarios.
• Challenge video fails as it has 3 lines from histogram peak.
• SNOW I am from Buffalo, NY and this is out of experience. When the roads are shoveled after a snow, there are thin lines of snow left inbetween which look very similar to the lanes. The algorithm must identify and distinguish them from the actual lanes.

Other techniques to experiment

• Smoothing/averaging
• Different Color channels
• Explore different way of removing pixel noise.
• Low pass filtering.
• Laplacian filters
• Convolutions instead of histgram