SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs
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arXiv
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| Format: | Preprint |
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2025
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| author | Diwanji, Hitvarth Liao, Jing-Yan Tumu, Akshar Christensen, Henrik I. Vazquez-Chanlatte, Marcell Tsuchiya, Chikao |
| author_facet | Diwanji, Hitvarth Liao, Jing-Yan Tumu, Akshar Christensen, Henrik I. Vazquez-Chanlatte, Marcell Tsuchiya, Chikao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs Diwanji, Hitvarth Liao, Jing-Yan Tumu, Akshar Christensen, Henrik I. Vazquez-Chanlatte, Marcell Tsuchiya, Chikao Robotics Computer Vision and Pattern Recognition 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. |
| title | SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.02773 |