# Research on Driving Scenario Knowledge Graphs
## 4 Conclusions  
This study investigates the modeling methods of knowledge graphs in the domain of driving scenarios. It includes ontology construction, knowledge extraction and fusion, and analysis of knowledge reasoning models based on representation learning. Ontology construction standardizes domain knowledge. It ensures the wide applicability of concept categories. This process lays the foundation for subsequent knowledge extraction and fusion. In the data extraction and fusion phase, entity data were extracted from the Traffic Genome dataset. It was then integrated with the ontology layer. This facilitated the expansion and updating of the knowledge graph. Finally, we will publicly release the generated DSKG and related materials for use by both industry and academia.  
Furthermore, we conducted in-depth research on knowledge reasoning models based on representation learning, introducing several commonly used models and evaluating their performance in link prediction tasks. The analysis revealed that the Distmult model performed the best in metrics such as average ranking and hit rate. This finding validates the effectiveness of the Distmult model in knowledge embedding and supports its application in real-time scene understanding and driving decision-making. Additionally, it can also be applied in scenario-based testing and verification. Knowledge embedding models accurately detect scene similarity, reducing non-safety-critical scenarios and improving testing efficiency.  
In the discussion, we acknowledged the scalability and applicability of our method. However, we recognize several limitations. Firstly, our method still requires human intervention in the knowledge graph construction process, lacking full automation. Secondly, the performance of our method is subject to the quality and quantity of data, which may limit its adaptability to modeling complex scenarios. Therefore, in future work, we need to explore more automated and intelligent approaches to enhance the efficiency and scope of our method. Additionally, our method has not fully considered the correlations and transitions between different scenarios, suggesting future research into knowledge associations and transfer methods between different scenes.  
In conclusion, our proposed method provides a systematic framework for constructing and enriching a knowledge graph of driving scenarios, advancing research and development in intelligent connected vehicle technology. Future work will focus on addressing the current limitations of the method and exploring more advanced technologies and methods to further advance driving scenario analysis and decision-making.  
Conceptualization, C.Z.; methodology, C.Z.; software, C.Z.; validation, C.Z. and X.L.; formal analysis, D.W.; investigation, L.H.; resources, Y.L.; data curation, J.Y.; writing--original draft preparation, C.Z.; writing--review and editing, D.W. and X.L.; visualization, J.Y.; supervision, L.H.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data for this study are publicly available. Below is the link to access the dataset: dskg: [[https://github.com/ZhangCeGitHub/dskg/tree/main](https://github.com/ZhangCeGitHub/dskg/tree/main)]([https://github.com/ZhangCeGitHub/dskg/tree/main](https://github.com/ZhangCeGitHub/dskg/tree/main)) (accessed on 28 April 2024).  
The authors declare no conflicts of interest.