Advanced Lane Finding Project

Programming Language:

• Python

The goals 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.

A. Pipeline.

My pipline consists of 7 steps as follows:

• Step 1: Camera Calibration
• Step 2: Distortion correction
• Step 3: Detect lines based on color and gradient
• Step 4: Perspective transform
• Step 5: Detect lane lines
• Step 6: Determine the lane curvature
• Step 7: Determine vehicle offset from center

B. Visualized Results

1. Camera calibration and Distortion correction: Image distortion occurs when a camera looks at 3D objects in the real world and transforms them into a 2D image; this transformation isn’t perfect. Distortion changes what the shape and size of these 3D objects appear to be. The following is a sample of the processed results:

Input image	After Calibration
One can see that the lines on the left side of the image become straight after distortion.

2: Detect lines based on color and gradient: Detect lines of the current lane based on color and gradient.

• I first convert the image from RGB color space to HLS color space. The following shows the HLS color space.

H channel	L channel	S channel

• Then, I do color selection on S channel and x gradient on L channel. The following shows the combined result:

Input Imgae	Color_Gradient Result

• After that, I do open operation on the previous result to remove noise. One can see that the tiny noise is remove.

Input Imgae	Open Operation

4. Perspective transform: To detect lines, I convert the image to birdeye view. I chose the following source and destination points:

The following shows a sample. It is clear that two lines are parallel.

Input Imgae	Open Operation

5. Detect lane lines:

• First, I calculate histogram on vertical direction.

Input Imgae	Histogram

• Then, I detect lines and compute their polynomial functions.

Input Imgae	Detect Lines

6. Visualize final result:

Input Imgae	Detect Lines

Potential improvements

1. This pipeline may be not work perfectly if the line is not very clear. Thus, merging the results generated using different color space will improve the performance.
2. The perspective transform matrix may be not exactly same for different camera. Thus, we need a smarter way to calculate it automatically.