Detecting lane markings is a challenging task for vision-based systems due to uncontrolled lighting environments present on the roads. Road infrastructures surrounding the painted markings such as guardrails and curbs often reduce the accuracy of existing solutions. The mentioned infrastructure frequently behaves like lane markings increasing the occurrence of candidate features to be selected by lane detectors. Most of the lane detectors use machine learning techniques with long training phases and inflexible models to achieve some level of robustness, therefore an efficient approach capable of performing unsupervised learn- ing is required. The adoption of an efficient model, which can monitor the ego-lane boundaries while identifying false positive references, is discussed in this paper. The proposed architecture allows the combination of multiple image-processing cues to improve accuracy and robustness on vision-based methods. Our method performed with high accuracy in lane marking detection under highly dynamic lighting, including in presence of guardrails.