SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs

High-definition maps (HD maps) are detailed and informative maps capturing lane centerlines and road elements. Although very useful for autonomous driving, HD maps are costly to build and maintain. Furthermore, access to these high-quality maps is usually limited to the firms that build them. On the other hand, standard definition (SD) maps provide road centerlines with an accuracy of a few meters. In this paper, we explore the possibility of enhancing SD maps by incorporating information from road manuals using LLMs. We develop SD++, an end-to-end pipeline to enhance SD maps with location-dependent road information obtained from a road manual. Our pipeline requires no sensor data input and only relies on road manuals and SD maps. We experiment several ways of using LLMs for map enhancement. Furthermore, we demonstrate the generalization ability of SD++ by showing results from six states in the United States and Japan. Code is available at https://github.com/AutonomousVehicleLaboratory/SDplusplus