<jupyter_start><jupyter_text>**Finding Lane Lines on the Road**  1. Describe your pipeline. As part of the description, explain how you modified the draw_lines() function.My pipeline consisted of 4 steps.1. First, I converted the images to grayscale, and then applied a gaussian blur  ![title](examples/solidWhiteRight_blur_gray.jpg) 2. A Canny edge detection was applied by canny function with defined low- and high threshold ![title](examples/solidWhiteRight_canny_image.jpg) 3. A trapezoid mask was applied to the image in order to distinguish the lane area. ![title](examples/solidWhiteRight_masked_image.jpg) 4. Apply the Hough Transform and process the lane lines out. ![title](test_images_output/solidWhiteRight.jpg)  2. Identify potential shortcomings with your current pipelineOne potential shortcoming would be what would happen when: - if the lanes is not stay in the middle area of the camera.- during the vehicle change to another courses.- if there is more lanes occur for example at cross road, then it might stuggling in finding the right lane. 3. Suggest possible improvements to your pipelineA possible improvement would be to- update the ROI mask dynamically- perform a segmentation of the road- Use Curves to filter the lanes instead of a straigt lane  Videos----------------------------------------------------------------------------------------  solidWhiteRight.mp4<jupyter_code>%%HTML
<video width="320" height="240" controls>
  <source src="test_videos_output/solidWhiteRight.mp4" type="video/mp4">
</video><jupyter_output><empty_output><jupyter_text>solidYellowLeft.mp4<jupyter_code>%%HTML
<video width="320" height="240" controls>
  <source src="test_videos_output/solidYellowLeft.mp4" type="video/mp4">
</video><jupyter_output><empty_output>
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