Shadow Removal in Traffic Lane Lines

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I am working on lane lines detection. My current working strategy is:

1. defining a region of interest where lane lines could be

2. Warping the image to get a bird eye view

3. Converting the image to YUV color space

4. Normalizing the Y channel

5. Fitting the second order polynomial and sliding window approach

every thing works fine but where there are shadows the algorithm do not work. I have tried adaptive thresholding, otssu thresholding but not succeeded.

Source Image without Shadow
Processed Source Image without Shadow

Source Image with Shadow
Processed Source Image with Shadow

In the second Image it can be seen that the shadowed area is not detected. Actually shadows drops the image values down so i tried to threshold the image with new values lower than the previous one new image can be found here

This technique does not work as it comes with a lot of noise

Currently I am trying background subtraction and shadow removal techniques but its not working. I am struck in this problem from last 2 3 weeks. Any help will really be appreciated...

import cv2
import matplotlib.pyplot as plt
import numpy as np 
from helper_functions import undistort, threshholding, unwarp,sliding_window_polyfit
from helper_functions import polyfit_using_prev_fit,calc_curv_rad_and_center_dist
from Lane_Lines_Finding import RoI
img = cv2.imread('./test_images/new_test.jpg')
new =undistort(img)
new = cv2.cvtColor(new, cv2.COLOR_RGB2BGR)
#new = threshholding(new)
h,w = new.shape[:2]
# define source and destination points for transform
imshape = img.shape
vertices = np.array([[
                      (257,670),
                      (590, 446),
                      (722, 440),
                      (1150,650)
                      ]], 
                      dtype=np.int32)  
p1 = (170,670)
p2 = (472, 475)
p3 = (745, 466)
p4 = (1050,650)

vertices = np.array([[p1,
                      p2,
                      p3,
                      p4
                      ]], 
                      dtype=np.int32)  
masked_edges = RoI(new, vertices)
#masked_edges = cv2.cvtColor(masked_edges, cv2.COLOR_RGB2BGR)


src = np.float32([(575,464),
                  (707,464), 
                  (258,682), 
                  (1049,682)])
dst = np.float32([(450,0),
                  (w-450,0),
                  (450,h),
                  (w-450,h)])
warp_img, M, Minv = unwarp(masked_edges, src, dst)
warp_img = increase_brightness_img(warp_img)
warp_img = contrast_img(warp_img)
YUV = cv2.cvtColor(warp_img, cv2.COLOR_RGB2YUV)
Y,U,V = cv2.split(YUV)
Y_equalized= cv2.equalizeHist(Y)
YUV = cv2.merge((Y,U,V))
thresh_min = 253
thresh_max = 255
binary = np.zeros_like(Y)
binary[(Y_equalized>= thresh_min) & (Y_equalized <= thresh_max)] = 1

kernel_opening= np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel_opening)
kernel= np.ones((7,7),np.uint8)
dilation = cv2.dilate(opening,kernel,iterations = 3)