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Article

Research on Driving Scenario Knowledge Graphs

College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(9), 3804; https://doi.org/10.3390/app14093804
Submission received: 28 March 2024 / Revised: 17 April 2024 / Accepted: 22 April 2024 / Published: 29 April 2024

Abstract

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.
Keywords: intelligent traffic; knowledge graph; hybrid methods; driving scenarios; ontology intelligent traffic; knowledge graph; hybrid methods; driving scenarios; ontology

Share and Cite

MDPI and ACS Style

Zhang, C.; Hong, L.; Wang, D.; Liu, X.; Yang, J.; Lin, Y. Research on Driving Scenario Knowledge Graphs. Appl. Sci. 2024, 14, 3804. https://doi.org/10.3390/app14093804

AMA Style

Zhang C, Hong L, Wang D, Liu X, Yang J, Lin Y. Research on Driving Scenario Knowledge Graphs. Applied Sciences. 2024; 14(9):3804. https://doi.org/10.3390/app14093804

Chicago/Turabian Style

Zhang, Ce, Liang Hong, Dan Wang, Xinchao Liu, Jinzhe Yang, and Yier Lin. 2024. "Research on Driving Scenario Knowledge Graphs" Applied Sciences 14, no. 9: 3804. https://doi.org/10.3390/app14093804

APA Style

Zhang, C., Hong, L., Wang, D., Liu, X., Yang, J., & Lin, Y. (2024). Research on Driving Scenario Knowledge Graphs. Applied Sciences, 14(9), 3804. https://doi.org/10.3390/app14093804

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