Maps for Autonomous Driving: Full-process Survey and Frontiers
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866911156578287616 |
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| author | Chen, Pengxin Luo, Zhipeng Jiang, Xiaoqi Yin, Zhangcai Li, Jonathan |
| author_facet | Chen, Pengxin Luo, Zhipeng Jiang, Xiaoqi Yin, Zhangcai Li, Jonathan |
| contents | Maps have always been an essential component of autonomous driving. With the advancement of autonomous driving technology, both the representation and production process of maps have evolved substantially. The article categorizes the evolution of maps into three stages: High-Definition (HD) maps, Lightweight (Lite) maps, and Implicit maps. For each stage, we provide a comprehensive review of the map production workflow, with highlighting technical challenges involved and summarizing relevant solutions proposed by the academic community. Furthermore, we discuss cutting-edge research advances in map representations and explore how these innovations can be integrated into end-to-end autonomous driving frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12632 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Maps for Autonomous Driving: Full-process Survey and Frontiers Chen, Pengxin Luo, Zhipeng Jiang, Xiaoqi Yin, Zhangcai Li, Jonathan Computer Vision and Pattern Recognition Maps have always been an essential component of autonomous driving. With the advancement of autonomous driving technology, both the representation and production process of maps have evolved substantially. The article categorizes the evolution of maps into three stages: High-Definition (HD) maps, Lightweight (Lite) maps, and Implicit maps. For each stage, we provide a comprehensive review of the map production workflow, with highlighting technical challenges involved and summarizing relevant solutions proposed by the academic community. Furthermore, we discuss cutting-edge research advances in map representations and explore how these innovations can be integrated into end-to-end autonomous driving frameworks. |
| title | Maps for Autonomous Driving: Full-process Survey and Frontiers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12632 |