Translating Images to Road Network: A Sequence-to-Sequence Perspective Article Swipe
Jiachen Lu
,
Renyuan Peng
,
Xinyue Cai
,
Hang Xu
,
Hongyang Li
,
Wen Feng
,
Wei Zhang
,
Li Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.08207
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.08207
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a significant challenge due to the conflicting underlying combination of Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road topological connectivity) structures. Existing methods struggle to merge the two types of data domains effectively, but few of them address it properly. Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence. Further than modeling an auto-regressive sequence-to-sequence Transformer model to understand RoadNet Sequence, we decouple the dependency of RoadNet Sequence into a mixture of auto-regressive and non-autoregressive dependency. Building on this, our proposed non-autoregressive sequence-to-sequence approach leverages non-autoregressive dependencies while fixing the gap towards auto-regressive dependencies, resulting in success in both efficiency and accuracy. We further identify two main bottlenecks in the current RoadNetTransformer on a non-overfitting split of the dataset: poor landmark detection limited by the BEV Encoder and error propagation to topology reasoning. Therefore, we propose Topology-Inherited Training to inherit better topology knowledge into RoadNetTransformer. Additionally, we collect SD-Maps from open-source map datasets and use this prior information to significantly improve landmark detection and reachability. Extensive experiments on the nuScenes dataset demonstrate the superiority of RoadNet Sequence representation and the non-autoregressive approach compared to existing state-of-the-art alternatives.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.08207
- https://arxiv.org/pdf/2402.08207
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391833549
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4391833549Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.08207Digital Object Identifier
- Title
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Translating Images to Road Network: A Sequence-to-Sequence PerspectiveWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-13Full publication date if available
- Authors
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Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Hongyang Li, Wen Feng, Wei Zhang, Li ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.08207Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2402.08207Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2402.08207Direct OA link when available
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Sequence (biology), Autoregressive model, STAR model, Computer science, Artificial intelligence, Time sequence, Econometrics, Mathematics, Autoregressive integrated moving average, Machine learning, Time series, Chemistry, BiochemistryTop 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.landmark | 169, 210 |
| abstract_inverted_index.modeling | 100 |
| abstract_inverted_index.nuScenes | 218 |
| abstract_inverted_index.proposed | 129 |
| abstract_inverted_index.struggle | 54 |
| abstract_inverted_index.topology | 180, 190 |
| abstract_inverted_index.Euclidean | 40, 87 |
| abstract_inverted_index.Extensive | 214 |
| abstract_inverted_index.Sequence, | 109 |
| abstract_inverted_index.Sequence. | 97 |
| abstract_inverted_index.accuracy. | 150 |
| abstract_inverted_index.challenge | 32 |
| abstract_inverted_index.detection | 170, 211 |
| abstract_inverted_index.essential | 6 |
| abstract_inverted_index.knowledge | 191 |
| abstract_inverted_index.landmarks | 21, 43 |
| abstract_inverted_index.leverages | 133 |
| abstract_inverted_index.location) | 44 |
| abstract_inverted_index.properly. | 70 |
| abstract_inverted_index.resulting | 143 |
| abstract_inverted_index.Therefore, | 182 |
| abstract_inverted_index.dependency | 113 |
| abstract_inverted_index.efficiency | 148 |
| abstract_inverted_index.extraction | 1 |
| abstract_inverted_index.generating | 26 |
| abstract_inverted_index.generation | 9 |
| abstract_inverted_index.projecting | 85 |
| abstract_inverted_index.reasoning. | 181 |
| abstract_inverted_index.underlying | 37 |
| abstract_inverted_index.understand | 107 |
| abstract_inverted_index.Transformer | 104 |
| abstract_inverted_index.bottlenecks | 156 |
| abstract_inverted_index.combination | 38 |
| abstract_inverted_index.conflicting | 36 |
| abstract_inverted_index.demonstrate | 220 |
| abstract_inverted_index.dependency. | 124 |
| abstract_inverted_index.establishes | 74 |
| abstract_inverted_index.experiments | 215 |
| abstract_inverted_index.information | 206 |
| abstract_inverted_index.open-source | 199 |
| abstract_inverted_index.propagation | 178 |
| abstract_inverted_index.significant | 31 |
| abstract_inverted_index.structures. | 51 |
| abstract_inverted_index.superiority | 222 |
| abstract_inverted_index.topological | 49 |
| abstract_inverted_index.dependencies | 135 |
| abstract_inverted_index.effectively, | 63 |
| abstract_inverted_index.localization | 18 |
| abstract_inverted_index.Additionally, | 194 |
| abstract_inverted_index.alternatives. | 235 |
| abstract_inverted_index.connectivity) | 50 |
| abstract_inverted_index.dependencies, | 142 |
| abstract_inverted_index.non-Euclidean | 46, 89 |
| abstract_inverted_index.reachability. | 213 |
| abstract_inverted_index.significantly | 208 |
| abstract_inverted_index.representation | 77, 226 |
| abstract_inverted_index.auto-regressive | 102, 121, 141 |
| abstract_inverted_index.high-definition | 11 |
| abstract_inverted_index.non-overfitting | 163 |
| abstract_inverted_index.state-of-the-art | 234 |
| abstract_inverted_index.interconnections. | 24 |
| abstract_inverted_index.RoadNetTransformer | 160 |
| abstract_inverted_index.Topology-Inherited | 185 |
| abstract_inverted_index.non-autoregressive | 123, 130, 134, 229 |
| abstract_inverted_index.RoadNetTransformer. | 193 |
| abstract_inverted_index.sequence-to-sequence | 103, 131 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |