Road markings embody the rules of the road whilst capturing the upcoming road layout. These rules are diligently studied and applied to driving situations by human drivers who have read Highway Traffic driving manuals (road marking interpretation). An autonomous vehicle must however be taught to read the road, as a human might. This paper addresses the problem of automatically reading the rules encoded in road markings, by classifying them into seven distinct classes: single boundary, double boundary, separator, zig-zag, intersection, boxed junction and special lane. Our method employs a unique set of geometric feature functions within a probabilistic RUSBoost and Conditional Random Field (CRF) classification framework. This allows us to jointly classify extracted road markings. Furthermore, we infer the semantics of road scenes (pedestrian approaches and no drive regions) based on marking classification results. Finally, our algorithms are evaluated on a large real-life ground truth annotated dataset from our vehicle.