## 2 Architecture to Support Algorithms and Strategies of Road Lane Marking Recognition  
The ROADLANE is a modular architecture of object recognition designed to identify and recognize road lane markings. In addition, it enables the customization of the strategies and algorithms, complying with automotive requirements. Therefore, it can be used to provide additional functionalities to the advanced driver assistance system (ADAS) for safety and comfortable driving, such as lane departure warning, lane-keeping assistance, adaptive cruise control, and automated brake assistance. The proposed architecture comprises three main layers, or stages, where the image signal processing identifies and recognizes patterns, such as road lanes, vehicles, and pedestrians. The processing flow is carried out from the bottom to the top. The following three stages are defined: (1) pre-processing, (2) processing, and (3) post-processing, as shown in Figure 2. A digital camera acquires the road image, which is in the lower level of the architecture. The features that use the image processed are on the upper level. As mentioned in the previous paragraph, these are the functions of our interest. The right side of Figure 2 shows the input image from the digital camera, without processing, and the output image with added information regarding the object detection over the image, delivering features necessary to implement the desired functionality such as LDW, LKA, and Driver Drowsiness System (DDS). The modular architecture provides the developer with the freedom to choose the algorithms, giving more flexibility. In addition, the contribution of this approach is the capability to adapt the statistical method with RANSAC to ensure a verification to deliver a lane marking recognition. In the proposed architecture, each layer has the following responsibilities:  
* Pre-processing: primary operations, such as noise reduction, contrast enhancement, and sharpness. * Processing: information extracted from the image, such as edges, contours, and identified objects), and
* Post-processing: electronic interpretation set of objects identified by cognitive functions usually associated with vision, as with computer vision [19; 20]. The camera must be fixed inside the vehicle (Figure 3a) and positioned on the windshield's upper left, coupled with the rearview mirror (Figure 3b). The camera's resolution can be from 320 \(\times\) 240 to 1200 \(\times\) 1080 pixels, depending on the requirements of the target application. We consider that there is a tradeoff between resolution and FOV, which we defined the default setup with 1200 \(\times\) 1080 pixels in fixed focus. This is sufficient to extract all the features of the image and video in data acquisition to be pre-processed. This is our strategy in calibration and use of the camera. The road environment image data captured by the frontal vehicle camera is streamed in real-time to an onboard vehicle computer and processed by ROADLINE architecture. The output of ROADLINE provides data for DAS applications with the objects of interest identified and classified, leaving it to the designer to specify the configuration according to the desired functionality. The front camera-based system is used in road vehicles to provide the functionalities related to the DAS, which can analyze the image content and extract features of interest. Our target example is the LKA, a feature that can assist the driver in keeping the vehicle within a lane by pushing it back by turning the steering wheel. It is effective because when the system verifies whether the vehicle is in the lane departure, the electronic steering unit  
Figure 3: (**a**) Positioning the camera sideways; (**b**) front camera positioning. Figure 2: The ROADLINE architecture for road lane marking recognition. can control the vehicle back to the correct position on the lane. If the steering pushback is not enough, the LKA function warns the driver to regain vehicle control.