Abstract
To solve the problems of difficult quantification of complex driving scenes and unclear classification, a method of complex measurement and scene classification was proposed. Based on the Bayesian network, the posterior probability distribution was obtained, the variable weights were determined by information entropy theory and BP neural network, and the gravitational model was improved so that the complex metric model of the driving scene was established, the static and dynamic complexity of the scene was quantified respectively, and a weighted fusion of the two was conducted. The K-means clustering method was used to divide the driving scenario into three categories, i.e., simple scenario, medium complex scenario, and complex scenario, and the rationality of the method was verified by experiments. This scenario complex metric method can provide a reference for studying the complex metrics and scene classification of smart vehicle test scenarios.
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Data Availability
The data that support the findings of this study are available on request from the corresponding author.
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Acknowledgements
This work was sponsored by the Project of State Administration for Market Regulation (202289), the Project of National Automobile Accident In-depth Investigation System (202248), the Project of Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province in China (QCCK 2021-011). This work was supported by the NAIS database and the China-PCS.
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Dong, X., Zhang, D., Mu, Y. et al. Complexity of Driving Scenarios Based on Traffic Accident Data. Int.J Automot. Technol. 25, 23–36 (2024). https://doi.org/10.1007/s12239-024-00004-y
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DOI: https://doi.org/10.1007/s12239-024-00004-y