As automated driving technologies become more mature, there is an increasing reliance on digital maps to support safe and efficient driving. Sensors like cameras and radars can be limited by occlusions, lighting conditions, or weather, and often fall short. High-definition (HD) maps offer excellent accuracy, but they are expensive to produce. These limitations make these techniques impractical for large-scale deployment. What makes our approach particularly attractive is its hardware simplicity: the entire process requires only a precise GNSS receiver and a commonly available lane detection camera, eliminating the need for expensive sensors like LiDAR or complex multi-vehicle fleets. We rigorously evaluated our method in a highway environment, where a vehicle equipped with our generated maps successfully executed autonomous lane following and adapted its speed based on detected speed limit signs. The positional deviation of the resulting maps was consistently under 5 cm.