This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach that combines 2D detections in grayscale images with stereo-vision based 3D validations in order to increase the robustness and accuracy of the results. Model based reasoning is also used to eliminate false positive situations and to detect exactly the objects of interest (stop-lines, pedestrian or bicycle crossings). The detected horizontal road markings can be used as landmarks for a very accurate longitudinal localization of the ego vehicle within intersection scenarios compensating missing GPS data or its lack of accuracy.