Construction Method for Knowledge Graph of Driving Behavior under Adverse Weather

Tian Yi, Yin Zhangcai, Chen Yiran
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:94-109, 2024.

Abstract

Weather condition are important factors affecting vehicle driving; However, the existing research results of weather factors on vehicle driving behavior are relatively scattered, and it is difficult to make effective decision analysis. Knowledge graph is one of the mainstream forms of knowledge base organization, which can clearly express the complex relationships between different objects. Therefore, introducing knowledge graph into driving behavior management is of great significance to improve decision making ability. This study analyzes the characteristics of driving behavior affected by weather, and constructs the knowledge expression model of “weather condition–environmental condition–driving behavior”. On this basis, a representation method based on characteristic value is proposed, and then the driving behavior knowledge graph is constructed. This method can clearly express the comprehensive relationship between weather condition and driving behavior, and provide support for vehicle driving behavior decision under complex weather condition. We take the existing relevant standards in the field of Meteorology and road traffic laws and regulations as examples to verify the practicability of the method. It lays a foundation for the construction of the knowledge graph of weather factors affecting driving behavior for autonomous vehicles in the future.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-yi24a, title = {Construction Method for Knowledge Graph of Driving Behavior under Adverse Weather}, author = {Yi, Tian and Zhangcai, Yin and Yiran, Chen}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {94--109}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/yi24a/yi24a.pdf}, url = {https://proceedings.mlr.press/v245/yi24a.html}, abstract = {Weather condition are important factors affecting vehicle driving; However, the existing research results of weather factors on vehicle driving behavior are relatively scattered, and it is difficult to make effective decision analysis. Knowledge graph is one of the mainstream forms of knowledge base organization, which can clearly express the complex relationships between different objects. Therefore, introducing knowledge graph into driving behavior management is of great significance to improve decision making ability. This study analyzes the characteristics of driving behavior affected by weather, and constructs the knowledge expression model of “weather condition–environmental condition–driving behavior”. On this basis, a representation method based on characteristic value is proposed, and then the driving behavior knowledge graph is constructed. This method can clearly express the comprehensive relationship between weather condition and driving behavior, and provide support for vehicle driving behavior decision under complex weather condition. We take the existing relevant standards in the field of Meteorology and road traffic laws and regulations as examples to verify the practicability of the method. It lays a foundation for the construction of the knowledge graph of weather factors affecting driving behavior for autonomous vehicles in the future. } }
Endnote
%0 Conference Paper %T Construction Method for Knowledge Graph of Driving Behavior under Adverse Weather %A Tian Yi %A Yin Zhangcai %A Chen Yiran %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-yi24a %I PMLR %P 94--109 %U https://proceedings.mlr.press/v245/yi24a.html %V 245 %X Weather condition are important factors affecting vehicle driving; However, the existing research results of weather factors on vehicle driving behavior are relatively scattered, and it is difficult to make effective decision analysis. Knowledge graph is one of the mainstream forms of knowledge base organization, which can clearly express the complex relationships between different objects. Therefore, introducing knowledge graph into driving behavior management is of great significance to improve decision making ability. This study analyzes the characteristics of driving behavior affected by weather, and constructs the knowledge expression model of “weather condition–environmental condition–driving behavior”. On this basis, a representation method based on characteristic value is proposed, and then the driving behavior knowledge graph is constructed. This method can clearly express the comprehensive relationship between weather condition and driving behavior, and provide support for vehicle driving behavior decision under complex weather condition. We take the existing relevant standards in the field of Meteorology and road traffic laws and regulations as examples to verify the practicability of the method. It lays a foundation for the construction of the knowledge graph of weather factors affecting driving behavior for autonomous vehicles in the future.
APA
Yi, T., Zhangcai, Y. & Yiran, C.. (2024). Construction Method for Knowledge Graph of Driving Behavior under Adverse Weather. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:94-109 Available from https://proceedings.mlr.press/v245/yi24a.html.

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