HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps Article Swipe
Mi Lu
,
Hang Zhao
,
Charlie Nash
,
Xiaohan Jin
,
Jiyang Gao
,
Chen Sun
,
Cordelia Schmid
,
Nir Shavit
,
Yuning Chai
,
Dragomir Anguelov
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.14880
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.14880
High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack to generalize onto new unseen scenarios. To address this issue, we introduce a new challenging task to generate HD maps. In this work, we explore several autoregressive models using different data representations, including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.14880
- https://arxiv.org/pdf/2106.14880
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3175957407
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3175957407Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2106.14880Digital Object Identifier
- Title
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HDMapGen: A Hierarchical Graph Generative Model of High Definition MapsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-28Full publication date if available
- Authors
-
Mi Lu, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir AnguelovList of authors in order
- Landing page
-
https://arxiv.org/abs/2106.14880Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2106.14880Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2106.14880Direct OA link when available
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Computer science, Scalability, Graph, Generative grammar, Autoregressive model, Baseline (sea), Semantics (computer science), Data mining, Key (lock), Theoretical computer science, Artificial intelligence, Mathematics, Programming language, Oceanography, Database, Geology, Econometrics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.precise | 7 |
| abstract_inverted_index.propose | 100 |
| abstract_inverted_index.several | 23, 85 |
| abstract_inverted_index.system, | 30 |
| abstract_inverted_index.through | 115 |
| abstract_inverted_index.traffic | 17 |
| abstract_inverted_index.HDMapGen | 130, 139 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.achieves | 140 |
| abstract_inverted_index.baseline | 133 |
| abstract_inverted_index.critical | 21 |
| abstract_inverted_index.generate | 77 |
| abstract_inverted_index.in-house | 126 |
| abstract_inverted_index.methods. | 134 |
| abstract_inverted_index.Argoverse | 122 |
| abstract_inverted_index.HDMapGen, | 101 |
| abstract_inverted_index.approach. | 118 |
| abstract_inverted_index.different | 89 |
| abstract_inverted_index.including | 31, 92 |
| abstract_inverted_index.introduce | 71 |
| abstract_inverted_index.planning. | 35 |
| abstract_inverted_index.producing | 109 |
| abstract_inverted_index.semantics | 14 |
| abstract_inverted_index.sequence, | 93 |
| abstract_inverted_index.Definition | 1 |
| abstract_inverted_index.autonomous | 28 |
| abstract_inverted_index.generalize | 61 |
| abstract_inverted_index.generation | 105 |
| abstract_inverted_index.real-world | 44 |
| abstract_inverted_index.scenarios. | 65 |
| abstract_inverted_index.topologies | 46 |
| abstract_inverted_index.Experiments | 119 |
| abstract_inverted_index.challenging | 74 |
| abstract_inverted_index.definitions | 8 |
| abstract_inverted_index.demonstrate | 137 |
| abstract_inverted_index.efficiency. | 144 |
| abstract_inverted_index.forecasting | 33 |
| abstract_inverted_index.geometries, | 48 |
| abstract_inverted_index.outperforms | 132 |
| abstract_inverted_index.scalability | 142 |
| abstract_inverted_index.hierarchical | 97, 103 |
| abstract_inverted_index.high-quality | 110 |
| abstract_inverted_index.self-driving | 58 |
| abstract_inverted_index.Additionally, | 135 |
| abstract_inverted_index.significantly | 50, 131 |
| abstract_inverted_index.autoregressive | 86 |
| abstract_inverted_index.coarse-to-fine | 117 |
| abstract_inverted_index.representations, | 91 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |