Abstract: Road detection plays an important role in autonomous driving system. One of the greatest challenges for vision-based road detection is the presence of shadows and other vehicles. It’s particularly challenging to detect unstructured road when it has both shadowed and non-shadowed area. Shadows can cause a significant problem in road detection since shadow boundaries may be incorrectly recognized or simply hinder the road detection process leading to a higher false rate detection. Therefore, shadow detection and removal is a crucial task in many computer vision applications. To tackle those issues, this study introduced an effective road recognition system using an image processing method to eliminate or reduce considerably the presence of strong shadows for unstructured road detection. The method’s main novelties are the use of a simple and effective shadow detection and removal algorithm using bilateral filter combined with a model-based classifier. Shadows were detected using normalized difference index and subsequent thresholding based on Otsu’s thresholding method. Furthermore, after the image-preprocessing step used for shadow removal, illumination invariant of road was estimated and a road probability map was calculated to determine whether or not each pixel belongs to road surface. Extensive experiments were carried out and the results showed that this method effectively detect unstructured road areas while being robust to strong shadows and illumination variations.