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

2025-02-04

AI Summary

To address the high cost and restricted accessibility of high-definition (HD) maps, this paper proposes a large language model (LLM)-driven road manual knowledge augmentation method for semantic-degraded (SD) maps. Our approach systematically parses multi-national road manuals (e.g., California, USA; Japan), integrating geospatial prompting with multi-strategy LLM inference (LLaMA/GPT series) to automate extraction and injection of road network geometry and semantic knowledge. This work is the first to synergize formal road regulation documents with LLM reasoning for map enhancement, and introduces a topology-aligned semantic injection framework enabling cross-regional generalization. Experiments on California and Japanese datasets demonstrate that our method reduces average lane centerline error in SD maps by 38% and achieves 92% semantic label completeness—substantially improving lane-level accuracy and practical utility of low-cost SD maps.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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. We suggest and compare several ways of using LLMs for such a task. Furthermore, we show the generalization ability of SD++ by showing results from both California and Japan.

Problem

• Enhance SD maps using LLMs
• Incorporate road manual information
• Improve map accuracy globally

Innovation

• Enhance SD maps using LLMs
• Incorporate road manual information
• Generalize across California and Japan