Lane identification in the autonomous vehicles Presenter: Aydin Ayanzadeh StudentID: Term project of image processing

Agenda ● INTRODUCTION ●Pipeline of the Project ●Results ●Conclusion 2

Introduction - PROJECT DESCRIPTION 3

Challenging images ❖ Areas of low lighting ❖ Areas of brightness ❖ Areas of obscured lane lines ❖ Areas of rapid curvature changes ❖ High Reflections from Windshield 4

Project pipeline 5

Camera calibration I.Camera architecture Camera perspective I.Distortion Correction ●Undistort camera image and 6

Project pipeline ●Perspective transformation 7

Project pipeline ●Convert to HSV color space and apply ●In the next step, a HSV color mask is applied to detect the white and yellow lanes ●color mask to identify yellow lines ●Combine binary masks 8

Lane detection ●Peak point in the histogram ●Slide the windows horizontally and vertically ●Polynomial Fitting 9

Results ●Good performance in straight line ●Can not fit to the curved road(polynomial regression has not implemented) 10

Results 11

Results ●bad quality of the lines, Shadows of the tree from the sun ●sharper curves in the road have impact on the detection of the lane ❏ Suggestions ●Adaptive threshold ●Polynomial coefficient matrix ●Deep learning methods 12

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