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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1569-8432 |