#### RANSAC Method for Lane Marking Recognition  
The LDW system aims to prevent vehicles from leaving their respective lanes and causing accidents. We might calculate the road's central medium line and threshold to serve as the reference by recognizing the lane marking. The gravity center of the vehicle should track the reference to let it drive safely. If the vehicle leaves the threshold, this could mean that the vehicle is in an unsafe condition, so the alert system can activate a warning message or even command the steering system to bring the vehicle back to a safe position. This work proposed the Random Sample Consensus (RANSAC) algorithm and other computer vision techniques to implement line marking recognition. To demonstrate the efficiency of the proposed algorithm, six different cases of variation in track lane marking are presented, as well as different drift variations.  
A recent investigation presented a lane marking recognition using the Hough Transform method [9], adopting the near and far-field concept in the captured image. This technique for LDW activation can be implemented in conjunction with lane marking tracking using RANSAC. Another investigation explored track identification using inverse perspective projection along with RANSAC and the Kalman filter application [10].  
Figure 1: Upper view of the digital camera’s range to recognize the lane markings on the road.  
[PERSON] et al. [11] proposed the multiple-track identification by linking the inverse perspective transformation, Hough transform, and a RANSAC code. [PERSON] et al. [12] applied [PERSON]'s method to implement a low-cost driver-assistance system consisting of two functions. The first function applies the Modified Extended Census Transformation and carries out the vehicle identification through classification. The second function consists of noise attenuation and application of the RANSAC code to identify lane markings. A robust ego-lane marking track detection system based on the stages of lane marking segmentation, recognition, and estimation through the RANSAC algorithm was proposed [13] to facilitate the implementation on real-time computing platforms. Reference [14] demonstrated a lane marking detection system through the modified RANSAC code merged with the least-squares method. Additionally, an algorithm emphasizing robustness and efficiency, considering the RANSAC application with the hyperbolic model, was presented [15].  
A lane marking detection system has been proposed using the application of small detection windows throughout the region of interest (ROI) [16] to binarize the image only in the windows that cover the behavior of the tracks. For the binarization process, the Otsu threshold method [17] with a preset threshold value of 50 ensured better border extraction and lower noise acquisition than the widely used Canny method [18]. Following the binarization process, the ROI was dived in the left and right parts for the application of RANSAC. This technique was applied in several climatic conditions and resulted in robust and reliable results.  
This paper is organized in the following structure: Section 2 describes the proposed architecture, labeled ROADLANE, which supports algorithms and strategies for road lane marking recognition. The architecture comprises three main layers from bottom to top (from the digital camera up to features): pre-processing, processing, and post-processing. Section 3 shows the pre-processing stages with several handlers to deal with the image acquisition from a digital camera. Section 4 shows the processing with the RANSAC method to extract the road lane identification and Section 5 shows the post-processing. Section 6 shows the results and discussions of six cases, considering a sample of over 4000 frames of our GSA (Grupo de Sistemas Automotivos--Automotive Systems Group) database. The selected samples present different climatic scenarios and road scenarios. It shows how the entire process is integrated to obtain an LDW feature. Finally, Section 7 presents the conclusion.