Overview / Usage

Advanced Lane Detection Project which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines. Also determined the real curvature of the lane and vehicle position with respect to center.

Methodology / Approach

The first step in the pipeline is to undistort the camera. Some images of a 9x6 chessboard are given and are distorted. Our task is to find the Chessboard corners an plot them. For this, after loading the images we calibrate the camera. Open CV functions like findChessboardCorners(), drawChessboardCorners() and calibrateCamera() help us do this.
Detecting edges around trees or cars is okay because these lines can be mostly filtered out by applying a mask to the image and essentially cropping out the area outside of the lane lines. It's most important that we reliably detect different colors of lane lines under varying degrees of daylight and shadow. So, that our self driving car does not become blind in extreme daylight hours or under the shadow of a tree.
I performed gradient threshold and color threshold individually and then created a binary combination of these two images to map out where either the color or gradient thresholds were met called the combined_binary in the code.
Once I got the Perspective Transform of the binary warped images, I first used the sliding window method to plot the lane lines and fitted a polynomial using fit_polynomial(img) function. Later on, I used the Search from prior technique and fitted a more accurate polynomial through my perspective transformed images using search_around_poly(image) funtion. Proper markings are there in the code to indicate each and every step.
For calculating the radius of curvature and the position of the vehicle with respect to center, I made a function called radius_and_offset(warped_image) which returns curvature_string and offset. Used left_lane_inds and right_lane_inds for performing the task. Used function fit_poly(image.shape, leftx, lefty, rightx, righty) which returns left_fitx, right_fitx, ploty to calcuate the real radius of curvature and offset.

Technologies Used

opencv2, deep learning

Repository

https://github.com/prateeksawhney97/Advanced-Lane-Finding-P2