Article
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Research on Driving Scenario Knowledge Graphs
Version 1
: Received: 3 April 2024 / Approved: 3 April 2024 / Online: 3 April 2024 (11:27:26 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, C.; Hong, L.; Wang, D.; Liu, X.; Yang, J.; Lin, Y. Research on Driving Scenario Knowledge Graphs. Appl. Sci. 2024, 14, 3804. Zhang, C.; Hong, L.; Wang, D.; Liu, X.; Yang, J.; Lin, Y. Research on Driving Scenario Knowledge Graphs. Appl. Sci. 2024, 14, 3804.
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 the 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
Subject
Engineering, Automotive Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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