用大模型从道路手册中提取知识，提升普通地图的精度
SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs
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高精地图（HD maps）包含车道中心线和道路元素，对自动驾驶至关重要，但构建和维护成本高昂，且通常仅限于少数企业使用。相比之下，标准定义（SD）地图仅提供几米级精度的道路中心线。本文探索利用大语言模型（LLMs）从道路手册中提取信息，以增强SD地图。提出SD++端到端框架，将来自道路手册的位置相关道路信息融入SD地图。对比多种基于LLM的实现方式，并验证其在加州和日本的泛化能力，结果表明该方法可有效提升地图质量。
原文摘要 · Abstract (English)
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.
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