A software pipeline to identify the lane boundaries in a video from a front-facing camera on a car
The 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.
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:
| Original | Undistorted |
|---|---|
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I used a combination of color and gradient thresholds to generate a binary image (thresholding code cell 6 and process(image function) in the notebook). I tried magnitude and direction thresholding but did not necessarily get better results (at times worse) so I removed it. For color threshold I use both HSL (S component) and HSV (V component) and then combine their result. Here's an example of my output for this step:
| Undistorted | Thresholded |
|---|---|
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The code for my perspective transform includes a function called warper() , which is in the code cell 7 of the IPython notebook. The warper() function takes as inputs an image ( image ) and calculates the src and dest points based on a region of interest defined in the function as offset from image edges. I define the region for the source and destination points using a trapezoidal region in the center of the image.
Here is an example of the transformed binary:
| Thresholded | Transformed |
|---|---|
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Then I did a convolution to find window centroids (code cell 8 and #4 and #5 in cell 9) where pixels were found. In this approach we slide a window template across the image from left to right and any overlapping values are summed together, creating the convolved signal. The peak of the convolved signal is where there was the highest overlap of pixels and the most likely position for the lane marker. Then I fit my lane lines with a 2nd order polynomial with the result (in code cell 9):
| Transformed | Tracked | Curve Fitted |
|---|---|---|
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R curve =( (1+(2Ay+B) 2 ) 3/2 )/ |2A|
I found the curvature for the left lane and used that. Another approach could have been find the curvatures for left and right and then average them or to find the curvature of the mid line.
| Curve Fitted | Mapped to road |
|---|---|
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This pipeline works well for the video provided in the project but it does not do well for the challenge video. The improvements would likely be in the thresholding part where more techniques and experimentation with thresholding values could result in a cleaner binary image which forms the basis for all computation after that. I also tried using the sliding window approach and due implementation difficulties I then followed the convolutions approach for finding the left and right lane in the transformed image.