A knowledge graph construction method based on co-occurrence for traffic entity prediction
Co-occurrence can improve the perception and prediction of traffic entities by predicting another traffic entity in automated driving based on a known entity. Existing traffic entity co-occurrence relationship construction methods use a bottom-up approach that relies on labeled datasets. However, th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003644 |
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| _version_ | 1839635725764526080 |
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| author | Zhangcai Yin Yiran Chen Jiangyan Gu Shen Ying Yuan Guo |
| author_facet | Zhangcai Yin Yiran Chen Jiangyan Gu Shen Ying Yuan Guo |
| author_sort | Zhangcai Yin |
| collection | DOAJ |
| description | Co-occurrence can improve the perception and prediction of traffic entities by predicting another traffic entity in automated driving based on a known entity. Existing traffic entity co-occurrence relationship construction methods use a bottom-up approach that relies on labeled datasets. However, the limited nature of datasets constrains the capture of long-tail scenarios. High-definition (HD) maps define the concept of traffic elements and their classifications, and can be used for scene simulation and real-time updating of digital twins. Based on this, this paper proposes a knowledge graph construction method for traffic entity prediction, employing a top-down approach and introducing HD maps. This method establishes a co-occurring semantic network between entities, utilizing prior knowledge. The objective is to reconcile the finiteness of datasets with the infinity of long-tailed scenarios, thereby providing a theoretical basis for the safety calibration of autonomous driving. The experimental results show that the knowledge graph constructed by the top-down method has a complete ontology type in the schema and takes into account the co-occurrence relations of the long-tailed scenarios, which provides a basis for the standardization of the co-occurrence relationships of the long-tailed scenarios, and also provides semantic support for the dynamic simulation of the digital twins in traffic scenes. |
| format | Article |
| id | doaj-art-4bcd44e56e4142608554fa6f57a58e23 |
| institution | Matheson Library |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-4bcd44e56e4142608554fa6f57a58e232025-07-09T04:32:04ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-01142104717A knowledge graph construction method based on co-occurrence for traffic entity predictionZhangcai Yin0Yiran Chen1Jiangyan Gu2Shen Ying3Yuan Guo4School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China; Corresponding authors.School of Resource and Environmental Sciences, Wuhan University, Wuhan 430070, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China; Corresponding authors.Co-occurrence can improve the perception and prediction of traffic entities by predicting another traffic entity in automated driving based on a known entity. Existing traffic entity co-occurrence relationship construction methods use a bottom-up approach that relies on labeled datasets. However, the limited nature of datasets constrains the capture of long-tail scenarios. High-definition (HD) maps define the concept of traffic elements and their classifications, and can be used for scene simulation and real-time updating of digital twins. Based on this, this paper proposes a knowledge graph construction method for traffic entity prediction, employing a top-down approach and introducing HD maps. This method establishes a co-occurring semantic network between entities, utilizing prior knowledge. The objective is to reconcile the finiteness of datasets with the infinity of long-tailed scenarios, thereby providing a theoretical basis for the safety calibration of autonomous driving. The experimental results show that the knowledge graph constructed by the top-down method has a complete ontology type in the schema and takes into account the co-occurrence relations of the long-tailed scenarios, which provides a basis for the standardization of the co-occurrence relationships of the long-tailed scenarios, and also provides semantic support for the dynamic simulation of the digital twins in traffic scenes.http://www.sciencedirect.com/science/article/pii/S1569843225003644Co-occurrence relationshipKnowledge graphTraffic entity predictionAutomated drivingDigital twinsHigh-definition maps |
| spellingShingle | Zhangcai Yin Yiran Chen Jiangyan Gu Shen Ying Yuan Guo A knowledge graph construction method based on co-occurrence for traffic entity prediction International Journal of Applied Earth Observations and Geoinformation Co-occurrence relationship Knowledge graph Traffic entity prediction Automated driving Digital twins High-definition maps |
| title | A knowledge graph construction method based on co-occurrence for traffic entity prediction |
| title_full | A knowledge graph construction method based on co-occurrence for traffic entity prediction |
| title_fullStr | A knowledge graph construction method based on co-occurrence for traffic entity prediction |
| title_full_unstemmed | A knowledge graph construction method based on co-occurrence for traffic entity prediction |
| title_short | A knowledge graph construction method based on co-occurrence for traffic entity prediction |
| title_sort | knowledge graph construction method based on co occurrence for traffic entity prediction |
| topic | Co-occurrence relationship Knowledge graph Traffic entity prediction Automated driving Digital twins High-definition maps |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003644 |
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